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Available online ,
doi: 10.11999/JEIT221201
Abstract:
The globalization of the Integrated Circuit(IC) supply chain has shifted most design, manufacturing, and testing processes from a single trusted entity to a variety of untrusted third-party entities in various parts of the world. The use of untrusted Third-Party Intellectual Property(3PIP) can expose a design to significant risk of having Hardware Trojans(HTs) implanted by adversaries. These hardware trojans may cause degradation of the original design, information leakage, or even irreversible damage at the physical level, seriously jeopardizing consumer privacy, security, and company reputation. Various hardware trojan detection approaches proposed in the existing literature have the following drawbacks: the reliance on golden reference model, the requirement for test vector coverage and even the need for manual code review. At the same time, with the increase of the scale of integrated circuits, the hardware trojans with low trigger rate are more difficult to be detected Therefore, to address the above problems, a graph neural network-based HT detection method is proposed that enables the detection of gate-level hardware trojans without the need for golden reference model as well as logic tests. Graph Sample and AGgrEgate(GraphSAGE) is used to learn the high-dimensional graph features in the gate-level netlist and the attributed node features. Then supervised learning is employed for the training of the detection model. The detection capability of models with different aggregation methods and data balancing methods are explored. An average recall of 92.9% and an average F1 score of 86.2% under the evaluation of the Synopsys 90 nm generic library(SAED) based benchmark in Trust-Hub are achieved by the model, which is an 8.4% improvement in F1 score compared to state of the art. When applied to the dataset with larger data volume based on 250 nm generic library(LEDA), the average recall and F1 of combined logic type are 83.6% and 70.8% respectively, and the average recall and F1 score of timing logic type are 95.0% and 92.8% respectively.
The globalization of the Integrated Circuit(IC) supply chain has shifted most design, manufacturing, and testing processes from a single trusted entity to a variety of untrusted third-party entities in various parts of the world. The use of untrusted Third-Party Intellectual Property(3PIP) can expose a design to significant risk of having Hardware Trojans(HTs) implanted by adversaries. These hardware trojans may cause degradation of the original design, information leakage, or even irreversible damage at the physical level, seriously jeopardizing consumer privacy, security, and company reputation. Various hardware trojan detection approaches proposed in the existing literature have the following drawbacks: the reliance on golden reference model, the requirement for test vector coverage and even the need for manual code review. At the same time, with the increase of the scale of integrated circuits, the hardware trojans with low trigger rate are more difficult to be detected Therefore, to address the above problems, a graph neural network-based HT detection method is proposed that enables the detection of gate-level hardware trojans without the need for golden reference model as well as logic tests. Graph Sample and AGgrEgate(GraphSAGE) is used to learn the high-dimensional graph features in the gate-level netlist and the attributed node features. Then supervised learning is employed for the training of the detection model. The detection capability of models with different aggregation methods and data balancing methods are explored. An average recall of 92.9% and an average F1 score of 86.2% under the evaluation of the Synopsys 90 nm generic library(SAED) based benchmark in Trust-Hub are achieved by the model, which is an 8.4% improvement in F1 score compared to state of the art. When applied to the dataset with larger data volume based on 250 nm generic library(LEDA), the average recall and F1 of combined logic type are 83.6% and 70.8% respectively, and the average recall and F1 score of timing logic type are 95.0% and 92.8% respectively.
Available online ,
doi: 10.11999/JEIT221223
Abstract:
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and downloads the most correlative of the k soft-predict. Then, the Shapley Value (SV) from collation game is applied to measure the multi-wise influences among clients and their marginal contribution to others on personalized learning performance is quantified. Lastly, each client identify it’s optimal coalition and then the Knowledge Distillation (KD) is used to local model and local training is conduct on the privacy dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and downloads the most correlative of the k soft-predict. Then, the Shapley Value (SV) from collation game is applied to measure the multi-wise influences among clients and their marginal contribution to others on personalized learning performance is quantified. Lastly, each client identify it’s optimal coalition and then the Knowledge Distillation (KD) is used to local model and local training is conduct on the privacy dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
Available online ,
doi: 10.11999/JEIT220645
Abstract:
In this paper, a Transfer Fuzzy C-Means clustering algorithm based on Maximum Mean Discrepancy (TFCM-MMD) is proposed. TFCM-MMD solves the problem that the transfer learning effect of the transfer fuzzy c-means clustering algorithm is weakened when the data distribution between source domain and target domain is very different. The algorithm measures inter-domain differences based on the maximum mean discrepancy criterion, and reduces the differences of data distribution between source domain and target domain in the common subspace by learning the projection matrix of source domain and target domain, so as to improve the effect of transfer learning. Finally, experiments based on synthetic datasets and medical image segmentation datasets further verify the effectiveness of TFCM-MMD algorithm in solving transfer clustering problems with large inter-domain differences.
In this paper, a Transfer Fuzzy C-Means clustering algorithm based on Maximum Mean Discrepancy (TFCM-MMD) is proposed. TFCM-MMD solves the problem that the transfer learning effect of the transfer fuzzy c-means clustering algorithm is weakened when the data distribution between source domain and target domain is very different. The algorithm measures inter-domain differences based on the maximum mean discrepancy criterion, and reduces the differences of data distribution between source domain and target domain in the common subspace by learning the projection matrix of source domain and target domain, so as to improve the effect of transfer learning. Finally, experiments based on synthetic datasets and medical image segmentation datasets further verify the effectiveness of TFCM-MMD algorithm in solving transfer clustering problems with large inter-domain differences.
Available online ,
doi: 10.11999/JEIT221193
Abstract:
Federated learning allows the effective flow of data value without leaving the local data, which is considered to be an effective way to balance data sharing and privacy protection in the Internet of Things (IoT) scenario. However, federated learning systems are vulnerable to Byzantine attacks and inference attacks, leading to the robustness of the system and the privacy of the data being compromised. The data heterogeneity and resource bottlenecks of IoT devices also pose significant challenges to the design of privacy-preserving and Byzantine-robust algorithms. In this paper, a data resampling of robust aggregation method Re-Sim applicable to heterogeneous IoT is proposed, which achieves robust aggregation by measuring directional similarity and normalized update magnitude, and uses data resampling to enhance robustness in data heterogeneous environments. Meanwhile, a Lightweight Security Aggregation (LSA) protocol is proposed to ensure data privacy while taking into account model robustness, accuracy and computational overhead, and theoretically the privacy of the protocol is analyzed. Simulation results show that the proposed scheme can effectively resist Byzantine attacks and inference attacks in the case of data heterogeneity. The proposed scheme improves the accuracy by 1%～3% compared to the baseline method, while reducing the client-side computational overhead by 79%.
Federated learning allows the effective flow of data value without leaving the local data, which is considered to be an effective way to balance data sharing and privacy protection in the Internet of Things (IoT) scenario. However, federated learning systems are vulnerable to Byzantine attacks and inference attacks, leading to the robustness of the system and the privacy of the data being compromised. The data heterogeneity and resource bottlenecks of IoT devices also pose significant challenges to the design of privacy-preserving and Byzantine-robust algorithms. In this paper, a data resampling of robust aggregation method Re-Sim applicable to heterogeneous IoT is proposed, which achieves robust aggregation by measuring directional similarity and normalized update magnitude, and uses data resampling to enhance robustness in data heterogeneous environments. Meanwhile, a Lightweight Security Aggregation (LSA) protocol is proposed to ensure data privacy while taking into account model robustness, accuracy and computational overhead, and theoretically the privacy of the protocol is analyzed. Simulation results show that the proposed scheme can effectively resist Byzantine attacks and inference attacks in the case of data heterogeneity. The proposed scheme improves the accuracy by 1%～3% compared to the baseline method, while reducing the client-side computational overhead by 79%.
Available online ,
doi: 10.11999/JEIT221265
Abstract:
The speed and frequency of underwater sound wave are much lower compared with the electromagnetic wave in the air. The moving target detection performance of multiple Unmanned Underwater Vehicle(multi-UUV) cooperative detection sonar is influenced seriously by the Doppler effect and narrowband signal processing. In this paper, the Discrete Frequency Coding Waveform of Non-Orthogonal Frequency Division Linear Frequency Modulation(DFCW-NOFD-LFM) based on Costas sequence and Orthogonal Frequency Division Linear Frequency Modulation(OFD-LFM) waveform is designed for multi-UUV cooperative detection sonar. The performance comparisons between the designed waveform and the traditional Code Division Multiple Access(CDMA) waveforms such as Binary Phase Shift Keying(BPSK), Discrete Frequency Coding Waveform(DFCW) are carried out. The results show that the designed DFCW-NOFD-LFM has large Doppler tolerance, good performance of auto-correlation, cross-correlation and reverberation suppression. It may be applied in the multi-UUV cooperative detection sonar to improve the relative moving target detection performance.
The speed and frequency of underwater sound wave are much lower compared with the electromagnetic wave in the air. The moving target detection performance of multiple Unmanned Underwater Vehicle(multi-UUV) cooperative detection sonar is influenced seriously by the Doppler effect and narrowband signal processing. In this paper, the Discrete Frequency Coding Waveform of Non-Orthogonal Frequency Division Linear Frequency Modulation(DFCW-NOFD-LFM) based on Costas sequence and Orthogonal Frequency Division Linear Frequency Modulation(OFD-LFM) waveform is designed for multi-UUV cooperative detection sonar. The performance comparisons between the designed waveform and the traditional Code Division Multiple Access(CDMA) waveforms such as Binary Phase Shift Keying(BPSK), Discrete Frequency Coding Waveform(DFCW) are carried out. The results show that the designed DFCW-NOFD-LFM has large Doppler tolerance, good performance of auto-correlation, cross-correlation and reverberation suppression. It may be applied in the multi-UUV cooperative detection sonar to improve the relative moving target detection performance.
Available online ,
doi: 10.11999/JEIT220918
Abstract:
A robust adaptive beamforming method based on covariance matrix reconstruction for a coprime array with gain/phase uncertainties is proposed. The main idea of this method is to reconstruct the covariance matrix of the signals. However, the accuracy of the reconstruction of the covariance matrix might be influenced by the gain/phase uncertainties . To eliminate the influence of the gain/phase uncertainties and accurately reconstruct the covariance matrix of the signals, a Total Least Squares (TLS) based method is proposed. First, the basic model of the covariance matrix reconstruction with gain/phase uncertainties is established. Then ,the problem is converted into an ErrorsIn Variables (EIV) model. The calibration of the gain/phase uncertainties is then converted into the estimation of an error matrix related to the gain/phase uncertainties. An alternating descent algorithm is developed to solve this problem. Simulation results showed that the proposed method can improve the accuracy of the reconstruction of the covariance matrix and is effective for adaptive beamforming.
A robust adaptive beamforming method based on covariance matrix reconstruction for a coprime array with gain/phase uncertainties is proposed. The main idea of this method is to reconstruct the covariance matrix of the signals. However, the accuracy of the reconstruction of the covariance matrix might be influenced by the gain/phase uncertainties . To eliminate the influence of the gain/phase uncertainties and accurately reconstruct the covariance matrix of the signals, a Total Least Squares (TLS) based method is proposed. First, the basic model of the covariance matrix reconstruction with gain/phase uncertainties is established. Then ,the problem is converted into an ErrorsIn Variables (EIV) model. The calibration of the gain/phase uncertainties is then converted into the estimation of an error matrix related to the gain/phase uncertainties. An alternating descent algorithm is developed to solve this problem. Simulation results showed that the proposed method can improve the accuracy of the reconstruction of the covariance matrix and is effective for adaptive beamforming.
Available online ,
doi: 10.11999/JEIT220811
Abstract:
The low transmission power and low signal-to-noise ratio increase the challenges of target detection for compact high-frequency surface wave radar. Track fragmentation often occurs due to missed detections during the tracking procedure. An adaptive weak target detection method using joint detection and tracking is suggested to enhance its detection performance. The tracker will communicate back the current target prediction state to the detector when it discovers that a target track cannot be connected to any new plot. The detector establishes a local detection gate on the range-Doppler spectrum, and the detection background is perceived using the binary hypothesis test. According to the detected background, an appropriate detection threshold adjustment method is used to lower the CFAR detection threshold and determines whether a weak target can be detected. The newly generated plot is obtained after the azimuth estimate and transmitted to the tracker for additional processing if a target is detected. The experimental results with field data reveal that the track length obtained by the proposed method is 29.76% longer than that of the detection before tracking methods, and the tracking time increases by 19.25 minutes on average.
The low transmission power and low signal-to-noise ratio increase the challenges of target detection for compact high-frequency surface wave radar. Track fragmentation often occurs due to missed detections during the tracking procedure. An adaptive weak target detection method using joint detection and tracking is suggested to enhance its detection performance. The tracker will communicate back the current target prediction state to the detector when it discovers that a target track cannot be connected to any new plot. The detector establishes a local detection gate on the range-Doppler spectrum, and the detection background is perceived using the binary hypothesis test. According to the detected background, an appropriate detection threshold adjustment method is used to lower the CFAR detection threshold and determines whether a weak target can be detected. The newly generated plot is obtained after the azimuth estimate and transmitted to the tracker for additional processing if a target is detected. The experimental results with field data reveal that the track length obtained by the proposed method is 29.76% longer than that of the detection before tracking methods, and the tracking time increases by 19.25 minutes on average.
Available online ,
doi: 10.11999/JEIT220980
Abstract:
In view of the problems that the current mainstream cross-modal image retrieval algorithm based on Convolutional Neural Network (CNN) paradigm cannot extract details of ship images effectively, and the cross-modal "heterogeneous gap" is difficult to eliminate, a Discriminant Adversarial Hash Transformer(DAHT) is proposed for fast cross-modal retrieval of ship images. The network adopts dual-stream Vision Transformer(ViT) structure and relies on the self-attention mechanism of ViT to extract the discriminant features of ship images. Based on this, a Hash Token structure is designed for Hash generation. In order to eliminate the cross-modal difference of the same category image, the whole retrieval framework is trained in an adversarial way, and modal confusion is realized by modal discrimination of generated hash codes. At the same time, a Normalized Discounted Cumulative Gain Weighting based Discriminant Cross-modal Quintuplet Loss (NW-DCQL) is designed to maintain the semantic discrimination of different types of images. In the four types of cross-modal retrieval tasks carried out on two datasets, the proposed method achieves 9.8 %, 5.2 %, 19.7 %, and 21.6 % performance improvement compared with the suboptimal retrieval results (32 bit), and also has certain performance advantages in unimodal retrieval tasks.
In view of the problems that the current mainstream cross-modal image retrieval algorithm based on Convolutional Neural Network (CNN) paradigm cannot extract details of ship images effectively, and the cross-modal "heterogeneous gap" is difficult to eliminate, a Discriminant Adversarial Hash Transformer(DAHT) is proposed for fast cross-modal retrieval of ship images. The network adopts dual-stream Vision Transformer(ViT) structure and relies on the self-attention mechanism of ViT to extract the discriminant features of ship images. Based on this, a Hash Token structure is designed for Hash generation. In order to eliminate the cross-modal difference of the same category image, the whole retrieval framework is trained in an adversarial way, and modal confusion is realized by modal discrimination of generated hash codes. At the same time, a Normalized Discounted Cumulative Gain Weighting based Discriminant Cross-modal Quintuplet Loss (NW-DCQL) is designed to maintain the semantic discrimination of different types of images. In the four types of cross-modal retrieval tasks carried out on two datasets, the proposed method achieves 9.8 %, 5.2 %, 19.7 %, and 21.6 % performance improvement compared with the suboptimal retrieval results (32 bit), and also has certain performance advantages in unimodal retrieval tasks.
Available online ,
doi: 10.11999/JEIT220733
Abstract:
Based on the component characteristics of Hewlett Packard(HP) memristor, the mathematical relationship formula of HP memristor is analyzed. There is an incremental linear relationship between the internal state variables of HP memristor components and the value of memristor. The change of the value of HP memristor can be superimposed under applied voltage, and the conclusion is drawn that HP memristor circuit has linear superposition, The validity and correctness of the above conclusions are verified by PSpice circuit simulation, which provides theoretical analysis support for the use of the superposition theorem in linear circuits containing HP memristors and linear components.
Based on the component characteristics of Hewlett Packard(HP) memristor, the mathematical relationship formula of HP memristor is analyzed. There is an incremental linear relationship between the internal state variables of HP memristor components and the value of memristor. The change of the value of HP memristor can be superimposed under applied voltage, and the conclusion is drawn that HP memristor circuit has linear superposition, The validity and correctness of the above conclusions are verified by PSpice circuit simulation, which provides theoretical analysis support for the use of the superposition theorem in linear circuits containing HP memristors and linear components.
Available online ,
doi: 10.11999/JEIT220655
Abstract:
The analysis of pathological images is of great significance for the diagnosis and prognosis of gastric cancer. However, its clinical application still faces challenges such as low consistency of visual reading and large differences in multi-resolution images. To address these issues, a prognostic prediction method of gastric cancer based on ensemble deep learning of pathological images is proposed. First, the pathological images at different resolutions of patient are preprocessed by slicing and filtering. Then, the deep feature extraction and fusion of slices at different resolutions are carried out by using three deep learning methods, i.e., ResNet, MobileNetV3, and EfficientNetV2, respectively, which aims to obtain the single-resolution prediction results of individual classifier at patient level. Finally, a double-level ensemble strategy is used to fuse the prediction results of heterogeneous individual classifiers at different resolutions to obtain the patient-level prognostic prediction results. In the experiment, the pathological images of 250 gastric cancer patients are collected, and the prediction of distant metastases is used as an example for verification. The experimental results show that the prediction accuracy of the proposed method on the test set is 89.10%, the sensitivity is 89.57%, the specificity is 88.61%, and the Matthews correlation coefficient is 78.19%. Compared with the single-model prediction results, the prediction performance of the proposed method has been significantly improved, which can provide an important reference for the treatment and prognosis of gastric cancer patients.
The analysis of pathological images is of great significance for the diagnosis and prognosis of gastric cancer. However, its clinical application still faces challenges such as low consistency of visual reading and large differences in multi-resolution images. To address these issues, a prognostic prediction method of gastric cancer based on ensemble deep learning of pathological images is proposed. First, the pathological images at different resolutions of patient are preprocessed by slicing and filtering. Then, the deep feature extraction and fusion of slices at different resolutions are carried out by using three deep learning methods, i.e., ResNet, MobileNetV3, and EfficientNetV2, respectively, which aims to obtain the single-resolution prediction results of individual classifier at patient level. Finally, a double-level ensemble strategy is used to fuse the prediction results of heterogeneous individual classifiers at different resolutions to obtain the patient-level prognostic prediction results. In the experiment, the pathological images of 250 gastric cancer patients are collected, and the prediction of distant metastases is used as an example for verification. The experimental results show that the prediction accuracy of the proposed method on the test set is 89.10%, the sensitivity is 89.57%, the specificity is 88.61%, and the Matthews correlation coefficient is 78.19%. Compared with the single-model prediction results, the prediction performance of the proposed method has been significantly improved, which can provide an important reference for the treatment and prognosis of gastric cancer patients.
Available online ,
doi: 10.11999/JEIT220578
Abstract:
To realize the generation of the navigation knowledge and the running control driven by goal for the intelligent and autonomous vehicle, a biological inspired Goal-Oriented (GO) navigation model based on spatial exploration and construction of cognitive map is discussed in this paper. This model is made up of three parts, including spatial exploration, construction of cognitive map and control of goal-oriented navigation. During spatial exploration, the model from Grid Cells (GCs) to Place Cells (PCs) and visual place cells’ model are fused to represent current state, and Q-learning algorithm is used to build and update the state-action. As a result, the goal-oriented navigation knowledge is learned. Then, during the construction of cognitive map, the gravity center estimation principle is used to deal with the obtained spatial exploration knowledge, which can produce the direction information corresponding to the different place cells’ state. Finally, during goal-oriented navigation process, the vehicle controls its running direction based on the cognitive map. Therefore, the goal-oriented navigation can be realized. Simulation validates that this model is available. The vehicle can construct cognitive map after sufficient spatial exploration and realizes goal-oriented navigation based on the cognitive map. Besides, the vehicle can effectively avoid obstacles during running.
To realize the generation of the navigation knowledge and the running control driven by goal for the intelligent and autonomous vehicle, a biological inspired Goal-Oriented (GO) navigation model based on spatial exploration and construction of cognitive map is discussed in this paper. This model is made up of three parts, including spatial exploration, construction of cognitive map and control of goal-oriented navigation. During spatial exploration, the model from Grid Cells (GCs) to Place Cells (PCs) and visual place cells’ model are fused to represent current state, and Q-learning algorithm is used to build and update the state-action. As a result, the goal-oriented navigation knowledge is learned. Then, during the construction of cognitive map, the gravity center estimation principle is used to deal with the obtained spatial exploration knowledge, which can produce the direction information corresponding to the different place cells’ state. Finally, during goal-oriented navigation process, the vehicle controls its running direction based on the cognitive map. Therefore, the goal-oriented navigation can be realized. Simulation validates that this model is available. The vehicle can construct cognitive map after sufficient spatial exploration and realizes goal-oriented navigation based on the cognitive map. Besides, the vehicle can effectively avoid obstacles during running.
Available online ,
doi: 10.11999/JEIT220561
Abstract:
DNA storage technology provides a new way to tackle the problems of massive data storage and application, due to its high density, long durability, and low maintenance cost. To face massive data storage demand, DNA storage has to overcome the problem on how to organize, access and manipulate data files, that is, the design of file system. In this paper, future DNA storage file system model and its characteristics are studied according to computer file system model. Then, the research progress of file system of DNA storage is systematically reviewed. Finally, the perspectives on research direction of future DNA storage file system are discussed.
DNA storage technology provides a new way to tackle the problems of massive data storage and application, due to its high density, long durability, and low maintenance cost. To face massive data storage demand, DNA storage has to overcome the problem on how to organize, access and manipulate data files, that is, the design of file system. In this paper, future DNA storage file system model and its characteristics are studied according to computer file system model. Then, the research progress of file system of DNA storage is systematically reviewed. Finally, the perspectives on research direction of future DNA storage file system are discussed.
Available online ,
doi: 10.11999/JEIT221554
Abstract:
To solve the problem of low security and poor transmission quality in cellular communication systems caused by eavesdroppers, obstacles and channel uncertainties, a robust secure resource allocation algorithm for Intelligent Reflecting Surface (IRS)-assisted multi-antenna communication systems is proposed. Firstly, a robust resource allocation problem with bounded channel uncertainties is formulated by jointly optimizing the active beam of the base station, the passive beam of the IRS, meanwhile the secure rate constraint of legitimate users, the maximum transmit power constraint and the phase shift constraint are considered. Then, the original non-convex problem with parametric perturbation is transformed using S-procedure, successive convex approximation, alternating optimization and penalty function to obtain a deterministic convex optimization problem that can be solved directly. Finally, an iteration-based robust energy efficiency maximization algorithm is proposed. Simulation results show that the proposed algorithm has good energy efficiency and strong robustness.
To solve the problem of low security and poor transmission quality in cellular communication systems caused by eavesdroppers, obstacles and channel uncertainties, a robust secure resource allocation algorithm for Intelligent Reflecting Surface (IRS)-assisted multi-antenna communication systems is proposed. Firstly, a robust resource allocation problem with bounded channel uncertainties is formulated by jointly optimizing the active beam of the base station, the passive beam of the IRS, meanwhile the secure rate constraint of legitimate users, the maximum transmit power constraint and the phase shift constraint are considered. Then, the original non-convex problem with parametric perturbation is transformed using S-procedure, successive convex approximation, alternating optimization and penalty function to obtain a deterministic convex optimization problem that can be solved directly. Finally, an iteration-based robust energy efficiency maximization algorithm is proposed. Simulation results show that the proposed algorithm has good energy efficiency and strong robustness.
Available online ,
doi: 10.11999/JEIT221259
Abstract:
This paper employs the difference coarray structures of transmit/receive EMVS to enhance the multidimensional parameter estimation performance in bistatic EMVS-MIMO radar. The difference coarrays of transmit/receive EMVS are built through the high-order tensor operation for receiving data. First, a fifth-order tensor model with the difference coarray of the original transmits/receive EMVS can be obtained by applying the tensor permutation rule and generalized tensorization. Additionally, the repeated elements in the difference coarray can be removed by using two selection matrices, where the obtained degree of freedom of the difference coarray is twice that of the original array. Then, a third-order tensor model with the third way fixed at 36 can be developed by using the generalized tensorization again. Finally, the PARAFAC algorithm is adopted to effectively estimate the transmit/receive four-dimensional parameter. Simulation demonstrate that the difference coarray can efficiently enhance the multi-dimensional parameter estimation performance in bistatic EMVS-MIMO radar.
This paper employs the difference coarray structures of transmit/receive EMVS to enhance the multidimensional parameter estimation performance in bistatic EMVS-MIMO radar. The difference coarrays of transmit/receive EMVS are built through the high-order tensor operation for receiving data. First, a fifth-order tensor model with the difference coarray of the original transmits/receive EMVS can be obtained by applying the tensor permutation rule and generalized tensorization. Additionally, the repeated elements in the difference coarray can be removed by using two selection matrices, where the obtained degree of freedom of the difference coarray is twice that of the original array. Then, a third-order tensor model with the third way fixed at 36 can be developed by using the generalized tensorization again. Finally, the PARAFAC algorithm is adopted to effectively estimate the transmit/receive four-dimensional parameter. Simulation demonstrate that the difference coarray can efficiently enhance the multi-dimensional parameter estimation performance in bistatic EMVS-MIMO radar.
Available online ,
doi: 10.11999/JEIT220946
Abstract:
Reconfigurable Intelligent Surface (RIS) can be regarded as a ‘relay’ with special functions in communication network. It can cooperate with Non-Orthogonal Multiple Access (NOMA) system to construct a coordinated information transmission scheme. Considering the different Quality of Service (QoS) requirements of different user devices in the future Internet of Things (IoT) scenarios, a RIS-assisted multi-user NOMA communication system model is proposed. According to the different QoS requirements of two types of users (information users and energy users), a power allocation method based on iterative optimization is designed. This method minimizes the total transmit power of the system by jointly designing the phase-shift matrix of RIS, the beamforming of base station and the order of successive interference cancellation in NOMA system, so as to comprehensively reduce the energy consumption of base station. Simulation results show that compared with the scenario without RIS, RIS-assisted NOMA system can effectively reduce the energy consumption of base station. In the case with RIS, the energy consumption of the proposed power allocation method is significantly lower than that of random phase selection at RIS and zero-forcing beamforming at base station.
Reconfigurable Intelligent Surface (RIS) can be regarded as a ‘relay’ with special functions in communication network. It can cooperate with Non-Orthogonal Multiple Access (NOMA) system to construct a coordinated information transmission scheme. Considering the different Quality of Service (QoS) requirements of different user devices in the future Internet of Things (IoT) scenarios, a RIS-assisted multi-user NOMA communication system model is proposed. According to the different QoS requirements of two types of users (information users and energy users), a power allocation method based on iterative optimization is designed. This method minimizes the total transmit power of the system by jointly designing the phase-shift matrix of RIS, the beamforming of base station and the order of successive interference cancellation in NOMA system, so as to comprehensively reduce the energy consumption of base station. Simulation results show that compared with the scenario without RIS, RIS-assisted NOMA system can effectively reduce the energy consumption of base station. In the case with RIS, the energy consumption of the proposed power allocation method is significantly lower than that of random phase selection at RIS and zero-forcing beamforming at base station.
Column
- Special Topic on Heaven and Earth Integration Intelligent Networking Technology
- Wireless Communication and Internet of Things
- Radar, Sonar and Array Signal Processing
- Cryption and Information Security
- Image and Intelligent Information Processing
- Circuit and System Design
- Overviews
- Dataset
- Information of National Natural Science Foundation
Display Method:
2023, 45(2): 383-395.
doi: 10.11999/JEIT211393
Abstract:
Different from terrestrial fixed communication networks, new challenges are brought to the design of routing protocols and algorithms for satellite Internet because of the characteristics of highly dynamic nodes, limited on-board processing capabilities and periodic changes of network topology in satellite networks.The routing technology proposed by the academic community for satellite networks is sorted out comprehensively and the future research hotspots of satellite routing technology is given in this paper. Firstly, the architecture of the satellite network and the main routing protocols currently used in the satellite communication system are introduced, and the routing problem of the satellite optical communication network is also briefly introduced. Secondly, the routing algorithms are classified into centralized satellite routing, distributed satellite routing and hybrid satellite routing according to the management method of satellite nodes and the routing table generation method.Later the representative results of various satellite routing methods are introduced and their optimization goals and applicable scenarios are summarized in detail. Then, how to choose the appropriate satellite network scenarios and network requirements under different satellite network scenarios is summarized. Finally, the challenges faced by the current satellite routing technology and future research hotspots are described, and the current mainstream satellite network simulation platforms are introduced in the appendix.
Different from terrestrial fixed communication networks, new challenges are brought to the design of routing protocols and algorithms for satellite Internet because of the characteristics of highly dynamic nodes, limited on-board processing capabilities and periodic changes of network topology in satellite networks.The routing technology proposed by the academic community for satellite networks is sorted out comprehensively and the future research hotspots of satellite routing technology is given in this paper. Firstly, the architecture of the satellite network and the main routing protocols currently used in the satellite communication system are introduced, and the routing problem of the satellite optical communication network is also briefly introduced. Secondly, the routing algorithms are classified into centralized satellite routing, distributed satellite routing and hybrid satellite routing according to the management method of satellite nodes and the routing table generation method.Later the representative results of various satellite routing methods are introduced and their optimization goals and applicable scenarios are summarized in detail. Then, how to choose the appropriate satellite network scenarios and network requirements under different satellite network scenarios is summarized. Finally, the challenges faced by the current satellite routing technology and future research hotspots are described, and the current mainstream satellite network simulation platforms are introduced in the appendix.
2023, 45(2): 396-406.
doi: 10.11999/JEIT211400
Abstract:
In recent years, with the upsurging development of launching, satellite, telecommunication, and networks technologies, the form of space-based networks is undergoing qualitative changes. Low-Earth-Orbit (LEO) constellation network has become a new option for many application scenarios. With the development trend towards a space-based information infrastructure, the LEO constellation is required to possess features of global coverage, constellation-terrestrial integration, diversified carrying, continuous evolution, security, and controllability, which put forward higher requirements for the network architecture of LEO constellation in the future. Therefore, in this paper, the development status of related fields of LEO constellation networks is summarized, and the tendencies of LEO constellation network development are analyzed. On this basis, an “all-in-cloud” network architecture based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) is proposed, making the network architecture programmable, decoupled, and decentralized. Moreover, the network operation management is supported by intention-driven approaches, so as to realize the capabilities of flexible carrying, continuous evolution, and automatic management of the network. Finally, the technical direction that needs to be focused on is looked forward to in this paper.
In recent years, with the upsurging development of launching, satellite, telecommunication, and networks technologies, the form of space-based networks is undergoing qualitative changes. Low-Earth-Orbit (LEO) constellation network has become a new option for many application scenarios. With the development trend towards a space-based information infrastructure, the LEO constellation is required to possess features of global coverage, constellation-terrestrial integration, diversified carrying, continuous evolution, security, and controllability, which put forward higher requirements for the network architecture of LEO constellation in the future. Therefore, in this paper, the development status of related fields of LEO constellation networks is summarized, and the tendencies of LEO constellation network development are analyzed. On this basis, an “all-in-cloud” network architecture based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) is proposed, making the network architecture programmable, decoupled, and decentralized. Moreover, the network operation management is supported by intention-driven approaches, so as to realize the capabilities of flexible carrying, continuous evolution, and automatic management of the network. Finally, the technical direction that needs to be focused on is looked forward to in this paper.
2023, 45(2): 407-417.
doi: 10.11999/JEIT211457
Abstract:
In the Low Earth Orbit (LEO) scenario, traditional resource allocation schemes can cause unbalanced resource allocation in specific cells. A beam hopping resource allocation scheme of LEO based on Transfer Deep Reinforcement Learning (TDRL) is proposed in this paper. Firstly, considering on-board buffer information, service arrival status and channel status, a LEO resource allocation optimization model that supports beam hopping technology is proposed with the goal of minimizing the average delay of data packets. Secondly, in view of the dynamic variability of the LEO network, the dynamic and random change of communication resources and requirements are considered, then the Deep Q Network (DQN) algorithm is adopted, and its neural network is used as a nonlinear approximation function. Further, to realize and accelerate the convergence process of the Deep Reinforcement Learning (DRL) algorithm in other target tasks, the concept of Transfer Learning (TL) is introduced in this paper, which uses the scheduling task learned by the source satellite to find quickly the beam scheduling and power allocation strategy of the target satellite. The simulation results demonstrate that the algorithm can optimize the time slot allocation in the satellite service process while decreasing the average delay of data packets and improving the throughput and resource utilization efficiency of the system.
In the Low Earth Orbit (LEO) scenario, traditional resource allocation schemes can cause unbalanced resource allocation in specific cells. A beam hopping resource allocation scheme of LEO based on Transfer Deep Reinforcement Learning (TDRL) is proposed in this paper. Firstly, considering on-board buffer information, service arrival status and channel status, a LEO resource allocation optimization model that supports beam hopping technology is proposed with the goal of minimizing the average delay of data packets. Secondly, in view of the dynamic variability of the LEO network, the dynamic and random change of communication resources and requirements are considered, then the Deep Q Network (DQN) algorithm is adopted, and its neural network is used as a nonlinear approximation function. Further, to realize and accelerate the convergence process of the Deep Reinforcement Learning (DRL) algorithm in other target tasks, the concept of Transfer Learning (TL) is introduced in this paper, which uses the scheduling task learned by the source satellite to find quickly the beam scheduling and power allocation strategy of the target satellite. The simulation results demonstrate that the algorithm can optimize the time slot allocation in the satellite service process while decreasing the average delay of data packets and improving the throughput and resource utilization efficiency of the system.
2023, 45(2): 418-426.
doi: 10.11999/JEIT220372
Abstract:
To compensate for the nonlinearity of the power amplifier in the RF front-end of high-capacity satellite communication, more coefficients and higher orders are required in the conventional Digital PreDistortion (DPD) model, which affects severely the resource consumption of the predistortion feedforward path. In this paper, a low-complexity DPD approach based on Improved Sparse Least Squares Twin Support Vector Regression (ISLSTSVR) modeling theory is presented to address this problem. Firstly, the problem that the solution of the Least Squares Twin Support Vector Regression(LSTSVR)model is not sparse is solved by constructing decision function in the original space; At the same time, the truncated least squares loss function is used to increase robustness of the model; Then the low-rank approximation of the kernel matrix is obtained by using Nystrom approximation method, and further Cholesky decomposition is used to reduce the operational complexity of the kernel matrix; Finally, the sparse solution of the model is obtained from low-rank kernel matrix. To verify the effectiveness of the proposed method, experiments are performed using a single-tube gallium nitride (GaN) broadband AB-class power amplifier with a 40 MHz 32 QAM signal for excitation. The experiment result shows that this method can greatly reduce the DPD model coefficients and computational complexity while ensuring the model accuracy, and provides an effective coefficient dimension reduction idea and method for the predistortion technology of the spaceborne RF front-end.
To compensate for the nonlinearity of the power amplifier in the RF front-end of high-capacity satellite communication, more coefficients and higher orders are required in the conventional Digital PreDistortion (DPD) model, which affects severely the resource consumption of the predistortion feedforward path. In this paper, a low-complexity DPD approach based on Improved Sparse Least Squares Twin Support Vector Regression (ISLSTSVR) modeling theory is presented to address this problem. Firstly, the problem that the solution of the Least Squares Twin Support Vector Regression(LSTSVR)model is not sparse is solved by constructing decision function in the original space; At the same time, the truncated least squares loss function is used to increase robustness of the model; Then the low-rank approximation of the kernel matrix is obtained by using Nystrom approximation method, and further Cholesky decomposition is used to reduce the operational complexity of the kernel matrix; Finally, the sparse solution of the model is obtained from low-rank kernel matrix. To verify the effectiveness of the proposed method, experiments are performed using a single-tube gallium nitride (GaN) broadband AB-class power amplifier with a 40 MHz 32 QAM signal for excitation. The experiment result shows that this method can greatly reduce the DPD model coefficients and computational complexity while ensuring the model accuracy, and provides an effective coefficient dimension reduction idea and method for the predistortion technology of the spaceborne RF front-end.
2023, 45(2): 427-435.
doi: 10.11999/JEIT220377
Abstract:
With the rapid development of Integrated Satellite-Terrestrial Networks (ISTNs), large scale sensors and wireless devices have the demand to access the wireless services, which is a new challenge to the spectrum efficiency and quality of service of ISTNs. Different from the conventional orthogonal multiple access technology, Non-Orthogonal Multiple Access (NOMA) can provide communication guarantee for multiple users in the same frequency. It is considered to be an effective technical path to improve the spectral efficiency of ISTNs, hence it has been widely studied. At present, most of the research on NOMA and ISTNs are investigated under perfect condition. Motivated by this condition, the performance of ISTNs with the more practical situation, i.e., imperfect Successive Interference Cancellation (SIC), channel estimation error and co-channel interference, is studied in this paper. Considering that both satellite and terrestrial users use multiple antennas, the closed-form expression of ergodic capacity is derived, which verifies the impact of imperfect conditions on the system performance. Meanwhile, Monte Carlo simulation is performed to verify the correctness of theoretical derivation results.
With the rapid development of Integrated Satellite-Terrestrial Networks (ISTNs), large scale sensors and wireless devices have the demand to access the wireless services, which is a new challenge to the spectrum efficiency and quality of service of ISTNs. Different from the conventional orthogonal multiple access technology, Non-Orthogonal Multiple Access (NOMA) can provide communication guarantee for multiple users in the same frequency. It is considered to be an effective technical path to improve the spectral efficiency of ISTNs, hence it has been widely studied. At present, most of the research on NOMA and ISTNs are investigated under perfect condition. Motivated by this condition, the performance of ISTNs with the more practical situation, i.e., imperfect Successive Interference Cancellation (SIC), channel estimation error and co-channel interference, is studied in this paper. Considering that both satellite and terrestrial users use multiple antennas, the closed-form expression of ergodic capacity is derived, which verifies the impact of imperfect conditions on the system performance. Meanwhile, Monte Carlo simulation is performed to verify the correctness of theoretical derivation results.
2023, 45(2): 436-445.
doi: 10.11999/JEIT220392
Abstract:
In Low Earth Orbit (LEO) satellite networks, the capacity of satellite payload is limited strictly, and onboard power resource is extremely scarce. Thus, a joint Beam Hopping (BH) scheduling and power allocation scheme is proposed to reduce onboard power resources consumption for the LEO system with BH antennas, while meeting quality of service requirements of users, so that the energy efficiency of the satellite communication system can be improved. Firstly, the joint beam scheduling and power allocation problem with delay constraints is formulated to minimize the power consumption of the satellite system. Considering the time-varying topological characteristics of the system, the original multi-slot optimization problem is transformed into a single-slot optimization problem based on the Lyapunov optimization method, and then the alternate optimization method is employed to obtain the sub-optimal solution of the single-slot problem. Specifically, the beam scheduling subproblem is proved to be convex, the power distribution subproblem is transformed into convex by successive convex approximation and logarithmic transformation, and the corresponding algorithm is proposed to obtain the optimal solution of the subproblems. Simulation results show that the proposed scheme can reduce the onboard power consumption of the satellite while ensuring the average time delay requirement of the users, and the dynamic balance of the delay and the power consumption can be achieved by adjusting the control parameter.
In Low Earth Orbit (LEO) satellite networks, the capacity of satellite payload is limited strictly, and onboard power resource is extremely scarce. Thus, a joint Beam Hopping (BH) scheduling and power allocation scheme is proposed to reduce onboard power resources consumption for the LEO system with BH antennas, while meeting quality of service requirements of users, so that the energy efficiency of the satellite communication system can be improved. Firstly, the joint beam scheduling and power allocation problem with delay constraints is formulated to minimize the power consumption of the satellite system. Considering the time-varying topological characteristics of the system, the original multi-slot optimization problem is transformed into a single-slot optimization problem based on the Lyapunov optimization method, and then the alternate optimization method is employed to obtain the sub-optimal solution of the single-slot problem. Specifically, the beam scheduling subproblem is proved to be convex, the power distribution subproblem is transformed into convex by successive convex approximation and logarithmic transformation, and the corresponding algorithm is proposed to obtain the optimal solution of the subproblems. Simulation results show that the proposed scheme can reduce the onboard power consumption of the satellite while ensuring the average time delay requirement of the users, and the dynamic balance of the delay and the power consumption can be achieved by adjusting the control parameter.
A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm
2023, 45(2): 446-454.
doi: 10.11999/JEIT220302
Abstract:
In traditional Digital PreDistortion (DPD) models, the same set of polynomial models and the same memory model are usually used to linearize the Power Amplifier (PA) at all input signal powers. However, the PAs exhibit different nonlinear characteristics and different memory effects at different power levels. In order to solve this problem, a DPD model based on the blind K-Nearest Neighbor (KNN) algorithm with dimension weighting is proposed. The input signal sequence is classified by the proposed model according to the magnitudes of amplifier's current input signal and the memory input signal with the dimension-weighted KNN classification .And sub-models are established for each type of input signal sequence. The proposed method is verified experimentally by a Doherty PA, a three carrier Long Term Evolution (LTE) signal with a bandwidth of 30 MHz and a frequency point of 2.2 GHz is used as the input, the feedback channel is sampled using a sampling rate of 122.88 MHz. When the dimensional-weighted blind KNN classification method is combined with the Memory Polynomial (MP) model, the forward modeling performance and digital pre-distortion performance for the PA which exceed the performance of Generalized Memory Polynomial (GMP) model and MP model are manifested in the experiment. The excellent performance of the proposed model is verified in the experiment.
In traditional Digital PreDistortion (DPD) models, the same set of polynomial models and the same memory model are usually used to linearize the Power Amplifier (PA) at all input signal powers. However, the PAs exhibit different nonlinear characteristics and different memory effects at different power levels. In order to solve this problem, a DPD model based on the blind K-Nearest Neighbor (KNN) algorithm with dimension weighting is proposed. The input signal sequence is classified by the proposed model according to the magnitudes of amplifier's current input signal and the memory input signal with the dimension-weighted KNN classification .And sub-models are established for each type of input signal sequence. The proposed method is verified experimentally by a Doherty PA, a three carrier Long Term Evolution (LTE) signal with a bandwidth of 30 MHz and a frequency point of 2.2 GHz is used as the input, the feedback channel is sampled using a sampling rate of 122.88 MHz. When the dimensional-weighted blind KNN classification method is combined with the Memory Polynomial (MP) model, the forward modeling performance and digital pre-distortion performance for the PA which exceed the performance of Generalized Memory Polynomial (GMP) model and MP model are manifested in the experiment. The excellent performance of the proposed model is verified in the experiment.
2023, 45(2): 455-463.
doi: 10.11999/JEIT211384
Abstract:
In view of the high dynamics in low-orbit satellite networks and complexity of space environment, the online provisioning of Service Function Chain (SFC) has become the key problem in satellite edge networks. Considering constraints in node and link capacity and switching costs in service migration, an online SFC deployment method based on natural gradient actor-critic reinforcement learning is proposed for low-orbit satellites equipped with Multi-access Edge Computing (MEC) servers. Firstly, the real-time capacity constraints and migration costs are formulated following the high environmental dynamics in low-orbit satellite networks, respectively. Secondly, involving the migration costs and satellite coordinates, Markov Decision Process (MDP) is introduced to describe the state transition in low-orbit satellite networks. Finally, a natural gradient method-based online SFC deployment method is proposed, which facilitates the training of neural network escaping from the local optimum as compared to the standard gradient. Simulation results show that proposed method could asymptotically approach the global optimum, and exceeds existing ones based on the standard gradient in terms of end-to-end delay.
In view of the high dynamics in low-orbit satellite networks and complexity of space environment, the online provisioning of Service Function Chain (SFC) has become the key problem in satellite edge networks. Considering constraints in node and link capacity and switching costs in service migration, an online SFC deployment method based on natural gradient actor-critic reinforcement learning is proposed for low-orbit satellites equipped with Multi-access Edge Computing (MEC) servers. Firstly, the real-time capacity constraints and migration costs are formulated following the high environmental dynamics in low-orbit satellite networks, respectively. Secondly, involving the migration costs and satellite coordinates, Markov Decision Process (MDP) is introduced to describe the state transition in low-orbit satellite networks. Finally, a natural gradient method-based online SFC deployment method is proposed, which facilitates the training of neural network escaping from the local optimum as compared to the standard gradient. Simulation results show that proposed method could asymptotically approach the global optimum, and exceeds existing ones based on the standard gradient in terms of end-to-end delay.
2023, 45(2): 464-471.
doi: 10.11999/JEIT211391
Abstract:
With the development of deep-space explorations in various countries, the demand for construction of space stations is increasing. However, a large number of data communication buses inside the spacecraft affect the payload of spacecraft to a certain extent. The wireless communication is introduced into spacecraft communication system. But, the traditional wireless communication can not guarantee the end-to-end delay for time-sensitive traffic. Therefore, this paper proposes a flow scheduling scheme for wire and wireless converged time-sensitive network. Firstly, a TDMA time slot allocation mechanism with separation of uplink and downlink communication is designed, the delay relationship between the type of services inside the spacecraft and the wired and wireless converged transmission link is modeling and analyzed. An objective function with the minimum average end-to-end delay of time-sensitive traffic is built. The time-slot allocation scheme is solved quickly by the particle swarm optimization algorithm. Finally, the proposed algorithm is tested on the Pycharm platform. Furthermore, a spacecraft sensor acquisition network is built on the EXata network simulation platform to test the performance. The results show that the proposed scheme can provide stable and bounded delay guarantees for time-sensitive traffic.
With the development of deep-space explorations in various countries, the demand for construction of space stations is increasing. However, a large number of data communication buses inside the spacecraft affect the payload of spacecraft to a certain extent. The wireless communication is introduced into spacecraft communication system. But, the traditional wireless communication can not guarantee the end-to-end delay for time-sensitive traffic. Therefore, this paper proposes a flow scheduling scheme for wire and wireless converged time-sensitive network. Firstly, a TDMA time slot allocation mechanism with separation of uplink and downlink communication is designed, the delay relationship between the type of services inside the spacecraft and the wired and wireless converged transmission link is modeling and analyzed. An objective function with the minimum average end-to-end delay of time-sensitive traffic is built. The time-slot allocation scheme is solved quickly by the particle swarm optimization algorithm. Finally, the proposed algorithm is tested on the Pycharm platform. Furthermore, a spacecraft sensor acquisition network is built on the EXata network simulation platform to test the performance. The results show that the proposed scheme can provide stable and bounded delay guarantees for time-sensitive traffic.
2023, 45(2): 472-479.
doi: 10.11999/JEIT211198
Abstract:
The space-based backbone transmission network will develop in the direction of broadband and relay integration, providing services such as global backbone transmission, broadband high-speed access and heterogeneous network interconnection for land, sea, air and space users. According to the service requirements of different users, a multi-layer satellite backbone network architecture for medium and high orbit (3GEO+3IGSO/24MEO) is proposed to use the "Satellite Backbone Network/All-Domain Users Access" model. Through the calculation and analysis of the spatial coverage of the architecture, it is concluded that the multi-layer satellite network can achieve 100% coverage of the whole airspace from the earth's surface to the Geostationary Earth Orbit altitude (about 36000 km), and provide multiple access capabilities for All-Domain users. Furthermore, through the analysis and comparison of the key network performance indicators such as path numbers, minimum hops and delay, the necessity of the existence of inter satellite links between medium orbit satellites and high orbit satellites in the architecture is proved. The analysis results show that the architecture can satisfy the broadband access and backbone transmission needs of all kinds of users in the whole airspace.
The space-based backbone transmission network will develop in the direction of broadband and relay integration, providing services such as global backbone transmission, broadband high-speed access and heterogeneous network interconnection for land, sea, air and space users. According to the service requirements of different users, a multi-layer satellite backbone network architecture for medium and high orbit (3GEO+3IGSO/24MEO) is proposed to use the "Satellite Backbone Network/All-Domain Users Access" model. Through the calculation and analysis of the spatial coverage of the architecture, it is concluded that the multi-layer satellite network can achieve 100% coverage of the whole airspace from the earth's surface to the Geostationary Earth Orbit altitude (about 36000 km), and provide multiple access capabilities for All-Domain users. Furthermore, through the analysis and comparison of the key network performance indicators such as path numbers, minimum hops and delay, the necessity of the existence of inter satellite links between medium orbit satellites and high orbit satellites in the architecture is proved. The analysis results show that the architecture can satisfy the broadband access and backbone transmission needs of all kinds of users in the whole airspace.
2023, 45(2): 480-487.
doi: 10.11999/JEIT211499
Abstract:
For Co-frequency and Co-time Full Duplex (CCFD) frequency hopping ad hoc network in finite area, communication performance analysis is proposed considering the scenario of self-interference and asymmetric mutual interference caused by unequal position of communication nodes. Taking the network band utilization as the performance index, the closed expression of network band utilization under the condition of node location distribution is derived, and a node location optimization distribution method reducing network mutual interference is proposed. Simulation results show that the performance of full duplex frequency hopping ad hoc network in finite area is strongly related to the number of frequency points, communication distance and the number of nodes. Besides, whether the performance of full duplex ad hoc network is better than half duplex network depends on the number of nodes.
For Co-frequency and Co-time Full Duplex (CCFD) frequency hopping ad hoc network in finite area, communication performance analysis is proposed considering the scenario of self-interference and asymmetric mutual interference caused by unequal position of communication nodes. Taking the network band utilization as the performance index, the closed expression of network band utilization under the condition of node location distribution is derived, and a node location optimization distribution method reducing network mutual interference is proposed. Simulation results show that the performance of full duplex frequency hopping ad hoc network in finite area is strongly related to the number of frequency points, communication distance and the number of nodes. Besides, whether the performance of full duplex ad hoc network is better than half duplex network depends on the number of nodes.
2023, 45(2): 488-496.
doi: 10.11999/JEIT211565
Abstract:
In traditional cellular networks, the randomness and uncertainty of channel attenuation lead to poor reception performance for cell-edge users, especially for video transmission and other high-speed requirements, its drawbacks are more prominent. D2D (Device-to-Device) communication can be used as a beneficial supplement to the traditional cellular network architecture because of its configuration flexibility, and the performance of edge users can be improved effectively. Aiming at the multicast transmission of D2D communication, the number of relays and the clustering algorithm are analyzed under the minimum delay cost of the system, and a low-latency D2D multicast scheme is proposed based on clustering and relay selection. This scheme can adaptively select the number of relays in the multicast retransmission and the distance from the cluster center to the base station, and at the same time, it provides an optimal bandwidth resource allocation mechanism. The simulation results show that compared with other schemes, the proposed strategy can effectively reduce the system delay and improve the edge user experience and system performance.
In traditional cellular networks, the randomness and uncertainty of channel attenuation lead to poor reception performance for cell-edge users, especially for video transmission and other high-speed requirements, its drawbacks are more prominent. D2D (Device-to-Device) communication can be used as a beneficial supplement to the traditional cellular network architecture because of its configuration flexibility, and the performance of edge users can be improved effectively. Aiming at the multicast transmission of D2D communication, the number of relays and the clustering algorithm are analyzed under the minimum delay cost of the system, and a low-latency D2D multicast scheme is proposed based on clustering and relay selection. This scheme can adaptively select the number of relays in the multicast retransmission and the distance from the cluster center to the base station, and at the same time, it provides an optimal bandwidth resource allocation mechanism. The simulation results show that compared with other schemes, the proposed strategy can effectively reduce the system delay and improve the edge user experience and system performance.
2023, 45(2): 497-504.
doi: 10.11999/JEIT211338
Abstract:
Because of the advantages of strong anti-interference ability and low probability of interception, Frequency Hopping (FH) is widely used in military and civilian fields. In view of the increasingly mature means of detecting FH and the information is easy to be intercepted, by drawing on the Orthogonal Frequency Division Multiplexing (OFDM) system framework, a Fractional Fourier Transform Frequency Hopping with Variable Time Wide and Fixed Bandwidth (FrFT-FH-VTFB) system based on Discrete Fractional Fourier Transform (DFrFT) is proposed in this paper and a new system framework is designed. While realizing the covert transmission of information, the DFrFT is used to avoid the problem that the hopping speed is limited by the frequency synthesizer. The system uses two PN sequences to select Chirp signals with different time width and frequency to achieve multi-dimensional transformation of system parameters. In addition, this paper establishes the relevant mathematical model and derives the theoretical bit error rate of the system under white noise channel. The simulation results show that the system designed in this paper has better anti-fading performance, the power spectrum is submerged under noise and the time-frequency domain characteristics have no obvious periodic characteristics.
Because of the advantages of strong anti-interference ability and low probability of interception, Frequency Hopping (FH) is widely used in military and civilian fields. In view of the increasingly mature means of detecting FH and the information is easy to be intercepted, by drawing on the Orthogonal Frequency Division Multiplexing (OFDM) system framework, a Fractional Fourier Transform Frequency Hopping with Variable Time Wide and Fixed Bandwidth (FrFT-FH-VTFB) system based on Discrete Fractional Fourier Transform (DFrFT) is proposed in this paper and a new system framework is designed. While realizing the covert transmission of information, the DFrFT is used to avoid the problem that the hopping speed is limited by the frequency synthesizer. The system uses two PN sequences to select Chirp signals with different time width and frequency to achieve multi-dimensional transformation of system parameters. In addition, this paper establishes the relevant mathematical model and derives the theoretical bit error rate of the system under white noise channel. The simulation results show that the system designed in this paper has better anti-fading performance, the power spectrum is submerged under noise and the time-frequency domain characteristics have no obvious periodic characteristics.
2023, 45(2): 505-515.
doi: 10.11999/JEIT211396
Abstract:
Focusing on the unknown and illegal detecting threat of wireless communication, a random path hopping scheme is proposed in this paper, which achieves random dynamic diffusion of signal energy on multipath. Firstly, a covert transmission strategy is constructed based on the multipath channel as well as the detecting model. Secondly, the closed expressions of the enemy's average received SNR (Signal to Noise Ratio) are derived respectively under the proposed scheme, the random path hopping scheme and the classic maximum ratio transmission scheme, and the minimal average covert probability is calculated by the curve fitting method for the qualitative and quantitative evaluation of covert performance. Finally, the closed expression of the legal receiver’s SNR is also derived for evaluating rate performance. The simulations reveal that the proposed scheme not only has the advantage of covert performance in the general case of enemy unknown, but also can solve the disabled problem of covert communication most effectively in the extreme case of enemy approaching the legal receiver.
Focusing on the unknown and illegal detecting threat of wireless communication, a random path hopping scheme is proposed in this paper, which achieves random dynamic diffusion of signal energy on multipath. Firstly, a covert transmission strategy is constructed based on the multipath channel as well as the detecting model. Secondly, the closed expressions of the enemy's average received SNR (Signal to Noise Ratio) are derived respectively under the proposed scheme, the random path hopping scheme and the classic maximum ratio transmission scheme, and the minimal average covert probability is calculated by the curve fitting method for the qualitative and quantitative evaluation of covert performance. Finally, the closed expression of the legal receiver’s SNR is also derived for evaluating rate performance. The simulations reveal that the proposed scheme not only has the advantage of covert performance in the general case of enemy unknown, but also can solve the disabled problem of covert communication most effectively in the extreme case of enemy approaching the legal receiver.
2023, 45(2): 516-523.
doi: 10.11999/JEIT211556
Abstract:
Orthogonal Chirp Division Multiplexing(OCDM) is a new multi-carrier system proposed in recent years. Through Fresnel transform, a set of orthogonal Chirp signals are obtained, which achieve the maximum spectral efficiency of CSS. In this paper, the basic principle of OCDM systems is introduced and the low complexity equalization algorithm of OCDM systems is studied. In doubly-selective channels, the performance of the traditional MMSE equalization algorithm declines. A Damped-LSQR algorithm is proposed based on approximate banded matrix, as a least square iterative algorithm for sparse matrix. To alleviate ICI in rapidly time-varying channels, an LSQR-BDFE algorithm is proposed based on approximate banded matrix. Combined with decision feedback equalization, LSQR algorithm is used for iterative calculation. The simulation results show that the OCDM system has better BER performance than the OFDM system under doubly-selective channels. The LSQR-BDFE algorithm and Band Damped-LSQR algorithm have performance advantages compared with the MMSE equalization algorithm.
Orthogonal Chirp Division Multiplexing(OCDM) is a new multi-carrier system proposed in recent years. Through Fresnel transform, a set of orthogonal Chirp signals are obtained, which achieve the maximum spectral efficiency of CSS. In this paper, the basic principle of OCDM systems is introduced and the low complexity equalization algorithm of OCDM systems is studied. In doubly-selective channels, the performance of the traditional MMSE equalization algorithm declines. A Damped-LSQR algorithm is proposed based on approximate banded matrix, as a least square iterative algorithm for sparse matrix. To alleviate ICI in rapidly time-varying channels, an LSQR-BDFE algorithm is proposed based on approximate banded matrix. Combined with decision feedback equalization, LSQR algorithm is used for iterative calculation. The simulation results show that the OCDM system has better BER performance than the OFDM system under doubly-selective channels. The LSQR-BDFE algorithm and Band Damped-LSQR algorithm have performance advantages compared with the MMSE equalization algorithm.
2023, 45(2): 524-533.
doi: 10.11999/JEIT211426
Abstract:
A great improvement in Underwater Acoustic Network (UAN) has been witnessed in past few years, but severe challenges still remain, and energy efficiency becomes the primary consideration of UAN. In addition, the reliability and effectiveness of underwater acoustic communication technology are seriously restricted by the large propagation delay of the underwater acoustic channel and the limitation of available bandwidth, and the performance of UAN is limited. Through the ability to focus a beam, the above challenges can be effectively addressed by directional communication technology, resulting in a higher communication range and signal-to-noise ratio than omnidirectional communication, as well as energy consumption efficiency and spatial reuse ratio of the whole network are improved. However, a priori knowledge of the location of the destination node is required and the problem of deafness occurs. Therefore, the Full-Duplex Directional Collision Avoidance (FDDCA) Medium Access Control (MAC) protocol is proposed in this paper, with which the problem of deafness is resolved by using two transducers that work in omnidirectional and directional modes, respectively, as well as the exposed terminal problem. Results supporting the conclusions are shown in the simulations, where 90% and 94% energy savings, 140% and 400% throughput improvements are acquired in different network topologies by FDDCA, compared with UnderWater Aloha (UW-Aloha) and Slotted FAMA (S-FAMA) protocol.
A great improvement in Underwater Acoustic Network (UAN) has been witnessed in past few years, but severe challenges still remain, and energy efficiency becomes the primary consideration of UAN. In addition, the reliability and effectiveness of underwater acoustic communication technology are seriously restricted by the large propagation delay of the underwater acoustic channel and the limitation of available bandwidth, and the performance of UAN is limited. Through the ability to focus a beam, the above challenges can be effectively addressed by directional communication technology, resulting in a higher communication range and signal-to-noise ratio than omnidirectional communication, as well as energy consumption efficiency and spatial reuse ratio of the whole network are improved. However, a priori knowledge of the location of the destination node is required and the problem of deafness occurs. Therefore, the Full-Duplex Directional Collision Avoidance (FDDCA) Medium Access Control (MAC) protocol is proposed in this paper, with which the problem of deafness is resolved by using two transducers that work in omnidirectional and directional modes, respectively, as well as the exposed terminal problem. Results supporting the conclusions are shown in the simulations, where 90% and 94% energy savings, 140% and 400% throughput improvements are acquired in different network topologies by FDDCA, compared with UnderWater Aloha (UW-Aloha) and Slotted FAMA (S-FAMA) protocol.
2023, 45(2): 534-541.
doi: 10.11999/JEIT211492
Abstract:
A novel grating lobe suppression method based on the difference in the relative position between the main lobe and grating lobes of different carrier frequency array patterns, is proposed to solve the problem of grating lobes in the array pattern due to the sparse array antenna spacing being larger than the signal wavelength. The algorithm makes use of the echo information of different carrier frequencies avoid large-scale search and reduce effectively the amount of computation. Firstly, the factors affecting the performance of the algorithm are determined theoretically. The key ones are then quantitatively analyzed and the relationship expression between the Peak SideLobe Ratio (PSLR) and frequency difference is deduced. It provides a theoretical basis for selecting quickly the optimal frequency difference for grating lobe suppression. Finally, the effectiveness of the algorithm for grating lobe suppression and the correctness of the expression of the relationship between the PSLR and frequency difference are verified by computer simulation.
A novel grating lobe suppression method based on the difference in the relative position between the main lobe and grating lobes of different carrier frequency array patterns, is proposed to solve the problem of grating lobes in the array pattern due to the sparse array antenna spacing being larger than the signal wavelength. The algorithm makes use of the echo information of different carrier frequencies avoid large-scale search and reduce effectively the amount of computation. Firstly, the factors affecting the performance of the algorithm are determined theoretically. The key ones are then quantitatively analyzed and the relationship expression between the Peak SideLobe Ratio (PSLR) and frequency difference is deduced. It provides a theoretical basis for selecting quickly the optimal frequency difference for grating lobe suppression. Finally, the effectiveness of the algorithm for grating lobe suppression and the correctness of the expression of the relationship between the PSLR and frequency difference are verified by computer simulation.
2023, 45(2): 542-549.
doi: 10.11999/JEIT211483
Abstract:
Compound-Gaussian distributions with Inverse Gaussian textures (IG-CG distributions) are commonly-used model to characterize high-resolution sea clutter and its parameter estimation plays an important role in adaptive target detection in high-resolution maritime radars. In parameter estimation, sea clutter data unavoidably contain a few of outliers from radar returns of sea-surface objects and reefs and in this case outlier-robust bi-percentile estimators are one of effective methods. This paper proposes an outlier-robust Tripercentile (Tri-per) estimation method, which is an improved version of the bi-percentile estimators. The improvement is made in two aspects. The positions of two sample percentiles are optimized to improve the estimation precision of the inverse shape parameter and the third sample percentile is introduced and its position is optimized to improve the estimation precision of the scale parameter. At last, simulated and measured data are used to verify the effectiveness and robustness of the proposed tri-percentile estimators.
Compound-Gaussian distributions with Inverse Gaussian textures (IG-CG distributions) are commonly-used model to characterize high-resolution sea clutter and its parameter estimation plays an important role in adaptive target detection in high-resolution maritime radars. In parameter estimation, sea clutter data unavoidably contain a few of outliers from radar returns of sea-surface objects and reefs and in this case outlier-robust bi-percentile estimators are one of effective methods. This paper proposes an outlier-robust Tripercentile (Tri-per) estimation method, which is an improved version of the bi-percentile estimators. The improvement is made in two aspects. The positions of two sample percentiles are optimized to improve the estimation precision of the inverse shape parameter and the third sample percentile is introduced and its position is optimized to improve the estimation precision of the scale parameter. At last, simulated and measured data are used to verify the effectiveness and robustness of the proposed tri-percentile estimators.
2023, 45(2): 550-557.
doi: 10.11999/JEIT211555
Abstract:
In recent years, the electromagnetic environment has been becoming increasingly complex and changeable, and new jamming methods emerge one after another, which brings great challenges and threats to the radar system. In this paper, the spectrum interference model is introduced and a transmit power allocation optimization method based on Reinforcement Learning (RL) under the dynamic game framework of Frequency Diverse Array Multi Input and Multi Output (FDA-MIMO) radar and the spectrum interference is proposed, so that the radar system can obtain the maximum output Signal-to-Interference plus Noise Ratio (SINR). Firstly, the mathematical model of FDA-MIMO radar is established, and on this basis, the spectrum interference model is constructed. Secondly, there is a Stackelberg game relationship between radar and jammer. Taking radar as the leader and jammer as the follower, the transmit power allocation optimization model under the framework of dynamic game is established. Using the Deep Deterministic Policy Gradient (DDPG) algorithm and power constraints, a reward function is designed to allocate the radar transmit power in real time to obtain the maximum output SINR. Finally, the simulation results show that under the framework of the game between radar and interference, the proposed optimization algorithm can effectively optimize the radar transmit power and make the radar have better anti-jamming performance.
In recent years, the electromagnetic environment has been becoming increasingly complex and changeable, and new jamming methods emerge one after another, which brings great challenges and threats to the radar system. In this paper, the spectrum interference model is introduced and a transmit power allocation optimization method based on Reinforcement Learning (RL) under the dynamic game framework of Frequency Diverse Array Multi Input and Multi Output (FDA-MIMO) radar and the spectrum interference is proposed, so that the radar system can obtain the maximum output Signal-to-Interference plus Noise Ratio (SINR). Firstly, the mathematical model of FDA-MIMO radar is established, and on this basis, the spectrum interference model is constructed. Secondly, there is a Stackelberg game relationship between radar and jammer. Taking radar as the leader and jammer as the follower, the transmit power allocation optimization model under the framework of dynamic game is established. Using the Deep Deterministic Policy Gradient (DDPG) algorithm and power constraints, a reward function is designed to allocate the radar transmit power in real time to obtain the maximum output SINR. Finally, the simulation results show that under the framework of the game between radar and interference, the proposed optimization algorithm can effectively optimize the radar transmit power and make the radar have better anti-jamming performance.
2023, 45(2): 558-566.
doi: 10.11999/JEIT211385
Abstract:
The traditional Uni-ElectroMagnetic-Vector-Sensor (UEMVS) is composed of three electric dipoles and three magnetic loops, and the pattern is omnidirectional. However, when multiple UEMVS are attached to the conformal platform to form a conformal UEMVS array, the pattern of each sensor is usually directional to reduce the sidelobe of the conformal UEMVS array. The UEMVS based on directional pattern is also called Uni-Direction-ElectroMagnetic-Vector-Sensor(UDEMVS). Considering the parameter estimation problem of UDEMVS, two parameter estimation methods, namely the Estimation of Signal Parameters via Rotational Invariant Technique and Vector Cross Product(ESPRIT-VCP) based on gridless search, and MUltiple SIgnal Classification and Minimum Rayleigh Quotient(MUSIC-MRQ) based on grid search, are proposed. The closed-form solutions of ESPRIT-VCP method can be obtained by the rotation invariance and vector cross product. The angle estimation results of MUSIC-MRQ method can be obtained by grid search and the minimum Rayleigh quotient model, and then the two-dimensional polarization parameters can be obtained by combining with the signal echo model. Using only six channel data of UDEMVS, the proposed two methods can effectively obtain the parameter estimation results, and have low computational complexity. Simulation results verify the effectiveness of the proposed method from the estimation performance of angle and polarization.
The traditional Uni-ElectroMagnetic-Vector-Sensor (UEMVS) is composed of three electric dipoles and three magnetic loops, and the pattern is omnidirectional. However, when multiple UEMVS are attached to the conformal platform to form a conformal UEMVS array, the pattern of each sensor is usually directional to reduce the sidelobe of the conformal UEMVS array. The UEMVS based on directional pattern is also called Uni-Direction-ElectroMagnetic-Vector-Sensor(UDEMVS). Considering the parameter estimation problem of UDEMVS, two parameter estimation methods, namely the Estimation of Signal Parameters via Rotational Invariant Technique and Vector Cross Product(ESPRIT-VCP) based on gridless search, and MUltiple SIgnal Classification and Minimum Rayleigh Quotient(MUSIC-MRQ) based on grid search, are proposed. The closed-form solutions of ESPRIT-VCP method can be obtained by the rotation invariance and vector cross product. The angle estimation results of MUSIC-MRQ method can be obtained by grid search and the minimum Rayleigh quotient model, and then the two-dimensional polarization parameters can be obtained by combining with the signal echo model. Using only six channel data of UDEMVS, the proposed two methods can effectively obtain the parameter estimation results, and have low computational complexity. Simulation results verify the effectiveness of the proposed method from the estimation performance of angle and polarization.
2023, 45(2): 567-575.
doi: 10.11999/JEIT211376
Abstract:
Considering the problem of real-time measurement of position and transmission of charge information in the recovery and guidance process of underwater high speed submersible, the acquisition and tracking technology based on the underwater continuous wave system is proposed. Continuous wave system is used to realize synchronous resolution of ranging and communication. The data acquisition time is compressed by parallel processing structure, and the optimal loop tracking strategy suitable for underwater acoustic environment and high dynamic background is designed based on the principle of phase-locked loop. From the theoretical simulation and Songhua Lake test results, the capture time of the algorithm is shortened from 83.87 s of the traditional matching algorithm to 0.66 s, and the calculation amount is reduced to 2.36 % of the time domain algorithm. Signal tracking technology has good performance under both high speed constant speed model and acceleration model. From the perspective of communication, the tracking algorithm can transmit data accurately. From the perspective of parameter estimation, the parameter accuracy based on tracking results changes slowly with the speed, but the traditional detection accuracy decreases with the increase of speed.This method achieves accurate Doppler estimation, and ensures the continuity and stability of ranging and communication. It is of great significance to the real-time recovery guidance of underwater high speed submersible.
Considering the problem of real-time measurement of position and transmission of charge information in the recovery and guidance process of underwater high speed submersible, the acquisition and tracking technology based on the underwater continuous wave system is proposed. Continuous wave system is used to realize synchronous resolution of ranging and communication. The data acquisition time is compressed by parallel processing structure, and the optimal loop tracking strategy suitable for underwater acoustic environment and high dynamic background is designed based on the principle of phase-locked loop. From the theoretical simulation and Songhua Lake test results, the capture time of the algorithm is shortened from 83.87 s of the traditional matching algorithm to 0.66 s, and the calculation amount is reduced to 2.36 % of the time domain algorithm. Signal tracking technology has good performance under both high speed constant speed model and acceleration model. From the perspective of communication, the tracking algorithm can transmit data accurately. From the perspective of parameter estimation, the parameter accuracy based on tracking results changes slowly with the speed, but the traditional detection accuracy decreases with the increase of speed.This method achieves accurate Doppler estimation, and ensures the continuity and stability of ranging and communication. It is of great significance to the real-time recovery guidance of underwater high speed submersible.
2023, 45(2): 576-584.
doi: 10.11999/JEIT211500
Abstract:
When airborne weather radar is used to detect low-altitude wind shear under complex terrain environment, ground clutter presents non-uniform characteristics and it is difficult to obtain enough Independent Identically Distributed (IID) samples, which affects the clutter suppression effect of Space-Time Adaptive Processing and makes the estimation of wind shear wind speed inaccurate. Based on the sparse characteristics of clutter signals, a Generalized Approximate Message Passing (GAMP) Space-Time Adaptive Processing (STAP) method is proposed in this paper. GAMP-STAP achieves accurate estimation of wind speed in complex terrain environment with only a small number of samples. Firstly, a sparse dictionary is constructed based on the prior information of the clutter ridge, then GAMP algorithm is used to estimate the clutter amplitude and recover the clutter power spectrum under the Bayesian framework, and then the clutter covariance matrix is calculated. Finally, STAP filter is constructed to achieve clutter suppression and wind shear wind speed estimation. Simulation results show the effectiveness of the proposed method.
When airborne weather radar is used to detect low-altitude wind shear under complex terrain environment, ground clutter presents non-uniform characteristics and it is difficult to obtain enough Independent Identically Distributed (IID) samples, which affects the clutter suppression effect of Space-Time Adaptive Processing and makes the estimation of wind shear wind speed inaccurate. Based on the sparse characteristics of clutter signals, a Generalized Approximate Message Passing (GAMP) Space-Time Adaptive Processing (STAP) method is proposed in this paper. GAMP-STAP achieves accurate estimation of wind speed in complex terrain environment with only a small number of samples. Firstly, a sparse dictionary is constructed based on the prior information of the clutter ridge, then GAMP algorithm is used to estimate the clutter amplitude and recover the clutter power spectrum under the Bayesian framework, and then the clutter covariance matrix is calculated. Finally, STAP filter is constructed to achieve clutter suppression and wind shear wind speed estimation. Simulation results show the effectiveness of the proposed method.
2023, 45(2): 585-591.
doi: 10.11999/JEIT211575
Abstract:
A multi-jammer cooperation track deception method against space detection radar network is proposed. Firstly, the constraint conditions are analyzed, and an ingenious cooperative flight idea of multi-jammer keeping the constant configuration during the exoatmospheric flying is proposed. Secondly, the initial state and jamming delay time of the multi-jammer are deduced and calculated. Then, the design flow and implementation flow of high fidelity track deception measures are given. Finally, a simulation experiment is carried out and a summary is made. The simulation results show that this method can form effective high fidelity deception tracks for the space detection radar network.
A multi-jammer cooperation track deception method against space detection radar network is proposed. Firstly, the constraint conditions are analyzed, and an ingenious cooperative flight idea of multi-jammer keeping the constant configuration during the exoatmospheric flying is proposed. Secondly, the initial state and jamming delay time of the multi-jammer are deduced and calculated. Then, the design flow and implementation flow of high fidelity track deception measures are given. Finally, a simulation experiment is carried out and a summary is made. The simulation results show that this method can form effective high fidelity deception tracks for the space detection radar network.
2023, 45(2): 592-599.
doi: 10.11999/JEIT211395
Abstract:
When positioning underwater volume target using traditional long baseline positioning model, the large geometric scale and unavailable attitude of the volume target always leads to model mismatch problems. In this paper, a long baseline high-precision positioning method for underwater volume target is proposed. The method eliminates the model error and realizes the approximate reduction of the geometric center of the volume target through the joint estimation of the attitude and position coordinates. Theoretical analysis and Numerical simulation results show that the large scale of the radius of the volume target affects the positioning accuracy. The proposed method achieved sub-meter accuracy positioning and improved the vertical positioning accuracy from 32 m to 0.5 m compared with the traditional long baseline positioning method under the conditions of a target radius of 5 m and a ranging accuracy of 0.2 m.
When positioning underwater volume target using traditional long baseline positioning model, the large geometric scale and unavailable attitude of the volume target always leads to model mismatch problems. In this paper, a long baseline high-precision positioning method for underwater volume target is proposed. The method eliminates the model error and realizes the approximate reduction of the geometric center of the volume target through the joint estimation of the attitude and position coordinates. Theoretical analysis and Numerical simulation results show that the large scale of the radius of the volume target affects the positioning accuracy. The proposed method achieved sub-meter accuracy positioning and improved the vertical positioning accuracy from 32 m to 0.5 m compared with the traditional long baseline positioning method under the conditions of a target radius of 5 m and a ranging accuracy of 0.2 m.
2023, 45(2): 600-607.
doi: 10.11999/JEIT211331
Abstract:
Using trust models to conduct trust evaluation is an efficient way to solve the security problem in distributed networks. However, most of the researches focus on collecting trust evidence completely or using new methods such as machine learning, blockchain to conduct trust evaluation. Few of the researches focus on how to obtain reliable initial trust of network nodes. In fact, many trust models for the distributed network rely on historical trust evidence, but the historical information is unavailable for the first trust evaluation. To address this problem, a trust evaluation method based on challenge-response model is proposed. First, the challenge-response model is leveraged to obtain a reliable initial trust. Then, the trust is used for trust evaluation process, including clustering, trust calculation and trust update. Simulation results show that the proposed method has better performance than the unified initialization trust based method, in terms of the prediction accuracy for malicious nodes and selfish nodes, as well as the detection rate for malicious nodes.
Using trust models to conduct trust evaluation is an efficient way to solve the security problem in distributed networks. However, most of the researches focus on collecting trust evidence completely or using new methods such as machine learning, blockchain to conduct trust evaluation. Few of the researches focus on how to obtain reliable initial trust of network nodes. In fact, many trust models for the distributed network rely on historical trust evidence, but the historical information is unavailable for the first trust evaluation. To address this problem, a trust evaluation method based on challenge-response model is proposed. First, the challenge-response model is leveraged to obtain a reliable initial trust. Then, the trust is used for trust evaluation process, including clustering, trust calculation and trust update. Simulation results show that the proposed method has better performance than the unified initialization trust based method, in terms of the prediction accuracy for malicious nodes and selfish nodes, as well as the detection rate for malicious nodes.
2023, 45(2): 608-616.
doi: 10.11999/JEIT211589
Abstract:
To deal with the difficulty of configuring Moving Target Defense (MTD) strategy under complexity attack scenarios in the cloud native environment, a deep reinforcement learning based moving target defense strategy optimization scheme (SmartSCR) is proposed. First, the security threats together with the attack paths are analyzed considering the characteristics of containerization and microservice. Then, in order to evaluate the defense efficiency of moving target defense under complexity attack scenarios in the cloud native environment, the microservice attack graph model is proposed to defense quantify efficiency. Finally, the optimization of moving target defense strategy is modeled as a Markov decision process. A deep reinforcement learning based strategy is proposed to handle the state space explosion under large scale cloud native applications, thus to solve out the optimal configuration for moving target defense strategy. The experiment results show that SmartSCR can quickly converge under large scale cloud native applications, and achieve near optimal defense efficiency.
To deal with the difficulty of configuring Moving Target Defense (MTD) strategy under complexity attack scenarios in the cloud native environment, a deep reinforcement learning based moving target defense strategy optimization scheme (SmartSCR) is proposed. First, the security threats together with the attack paths are analyzed considering the characteristics of containerization and microservice. Then, in order to evaluate the defense efficiency of moving target defense under complexity attack scenarios in the cloud native environment, the microservice attack graph model is proposed to defense quantify efficiency. Finally, the optimization of moving target defense strategy is modeled as a Markov decision process. A deep reinforcement learning based strategy is proposed to handle the state space explosion under large scale cloud native applications, thus to solve out the optimal configuration for moving target defense strategy. The experiment results show that SmartSCR can quickly converge under large scale cloud native applications, and achieve near optimal defense efficiency.
2023, 45(2): 617-625.
doi: 10.11999/JEIT211438
Abstract:
Subspace trail attack is a new analysis method for block ciphers. The properties of subspaces of 3D cipher which uses a new structure of AES-like ciphers is studied. First of all, a 3-round definite subspace trail of 3Dcipher is constructed in this paper, combined with the intersection property of subspaces, and the 7-round subspace trail impossible differential distinguisher of 3D cipher is obtained for the first time. Its data complexity is\begin{document}$ {2^{193.1}} $\end{document} ![]()
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Subspace trail attack is a new analysis method for block ciphers. The properties of subspaces of 3D cipher which uses a new structure of AES-like ciphers is studied. First of all, a 3-round definite subspace trail of 3Dcipher is constructed in this paper, combined with the intersection property of subspaces, and the 7-round subspace trail impossible differential distinguisher of 3D cipher is obtained for the first time. Its data complexity is
2023, 45(2): 626-634.
doi: 10.11999/JEIT211490
Abstract:
Key pre-distribution is one of the most challenging security problems in wireless sensor networks. In the paper, a new combinatorial design based on the orthogonal relation between the subspaces of symplectic space over finite fields is constructed, and a key pre-distribution scheme is constructed from the design. Let V be a subspace of type (4,2) in an 8-dimensional symplectic space over finite fields. A subspace of type (1,0) in V is regarded as a node in the key pre-distribution scheme, and all the subspaces of (2,1) in V is regarded as the key pool of the scheme. The whole target area is divided into a number of equally sized cells, each cell has normal nodes and cluster heads two types nodes. The key pre-distribution scheme from symplectic space is adopted to distribute keys to nodes of each cell, and different cells has different key pools, so nodes in different cells need to establish indirect communication through the cluster heads, the cluster heads in different cells distribute keys in a complete key pre-distribution scheme. Compared with other schemes, the advantages of the proposed scheme is the strong anti-compromise ability of nodes in the networks, and with the continuous expansion of the network scale, the connectivity gradually tends to 1.
Key pre-distribution is one of the most challenging security problems in wireless sensor networks. In the paper, a new combinatorial design based on the orthogonal relation between the subspaces of symplectic space over finite fields is constructed, and a key pre-distribution scheme is constructed from the design. Let V be a subspace of type (4,2) in an 8-dimensional symplectic space over finite fields. A subspace of type (1,0) in V is regarded as a node in the key pre-distribution scheme, and all the subspaces of (2,1) in V is regarded as the key pool of the scheme. The whole target area is divided into a number of equally sized cells, each cell has normal nodes and cluster heads two types nodes. The key pre-distribution scheme from symplectic space is adopted to distribute keys to nodes of each cell, and different cells has different key pools, so nodes in different cells need to establish indirect communication through the cluster heads, the cluster heads in different cells distribute keys in a complete key pre-distribution scheme. Compared with other schemes, the advantages of the proposed scheme is the strong anti-compromise ability of nodes in the networks, and with the continuous expansion of the network scale, the connectivity gradually tends to 1.
2023, 45(2): 635-643.
doi: 10.11999/JEIT211429
Abstract:
The abruptness, diversity, and deviation of public opinion information in mobile social networks may encourage malicious users to spread rumors and has a bad impact on the network environment. To solve this problem, a controllable information propagation method based on network motif is proposed. Firstly, a Multi-entity Competitive Independent Cascade (MCIC)model in the social network layer is established. Secondly, this paper defines the Control Information Flow Motif (CIFM), determine the key network motifs and designs its efficient and controllable propagation algorithm in the communication layer. Finally, Theoretical derivation proves that this method has convergence, and the simulation results show that the proposed method not only has more advantages in terms of time efficiency, but also has the best effect in controlling information propagation.
The abruptness, diversity, and deviation of public opinion information in mobile social networks may encourage malicious users to spread rumors and has a bad impact on the network environment. To solve this problem, a controllable information propagation method based on network motif is proposed. Firstly, a Multi-entity Competitive Independent Cascade (MCIC)model in the social network layer is established. Secondly, this paper defines the Control Information Flow Motif (CIFM), determine the key network motifs and designs its efficient and controllable propagation algorithm in the communication layer. Finally, Theoretical derivation proves that this method has convergence, and the simulation results show that the proposed method not only has more advantages in terms of time efficiency, but also has the best effect in controlling information propagation.
2023, 45(2): 644-653.
doi: 10.11999/JEIT211478
Abstract:
In this paper, a novel metric learning method based on the triplet sampling graph convolutional network is proposed to realize semi-supervised Content-Based Image Retrieval (CBIR) for remote sensing images. The proposed method consists of two parts: Triplet Graph Convolutional Network (TGCN) and Graph-based Triplet Sampling (GTS). TGCN is composed of three parallel convolutional neural networks and graph convolutional networks with shared weights to extract the initial features of the image and learn the graph embedding of the image. By learning simultaneously image features and graph embedding, TGCN can obtain an effective graph structure for semi-supervised image retrieval.Besides, the image similarity information implicit in the graph structure is evaluated by the proposed GTS algorithm to select the appropriate Hard triplet, and the sample set composed of the Hard triplet then can be used to train effectively and quickly the model. Through the combination of TGCN and GTS, the proposed metric learning method is tested on two remote sensing data sets. Experimental results show that TGCN-GTS has the following two advantages: TGCN can learn effective graph embedding features and metric space according to the image and graph structure; GTS evaluates effectively the image similarity information implicit in the image structure and then selects the appropriate Hard triplet, which improves significantly the retrieval performance of semi-supervised remote sensing images.
In this paper, a novel metric learning method based on the triplet sampling graph convolutional network is proposed to realize semi-supervised Content-Based Image Retrieval (CBIR) for remote sensing images. The proposed method consists of two parts: Triplet Graph Convolutional Network (TGCN) and Graph-based Triplet Sampling (GTS). TGCN is composed of three parallel convolutional neural networks and graph convolutional networks with shared weights to extract the initial features of the image and learn the graph embedding of the image. By learning simultaneously image features and graph embedding, TGCN can obtain an effective graph structure for semi-supervised image retrieval.Besides, the image similarity information implicit in the graph structure is evaluated by the proposed GTS algorithm to select the appropriate Hard triplet, and the sample set composed of the Hard triplet then can be used to train effectively and quickly the model. Through the combination of TGCN and GTS, the proposed metric learning method is tested on two remote sensing data sets. Experimental results show that TGCN-GTS has the following two advantages: TGCN can learn effective graph embedding features and metric space according to the image and graph structure; GTS evaluates effectively the image similarity information implicit in the image structure and then selects the appropriate Hard triplet, which improves significantly the retrieval performance of semi-supervised remote sensing images.
2023, 45(2): 654-663.
doi: 10.11999/JEIT211506
Abstract:
The traditional iterative-based Compressive Sensing (CS) image reconstruction algorithm is easy to integrate image prior information, but it has shortcomings such as insufficient performance and high computational complexity. The performance of the image reconstruction algorithm based on deep learning is better than the traditional reconstruction algorithm significantly, and it has lower time cost. Therefore, in order to design a deep learning image reconstruction algorithm that uses prior information more effectively, a deep compressive sensing image reconstruction network based on non-local priors is proposed. Firstly, the sparseness and non-local prior are combined to establish a compressed sensing image reconstruction model. Secondly, the model is decomposed into three sub-problems by the half quadratic splitting method. The solution of each sub-problem is carried out under the framework of deep learning. Finally, an end-to-end trainable image reconstruction model is jointly established. Simulation experiments show that the peak signal-to-noise ratio of the proposed algorithm under the tested sampling rate and dataset is improved by 0.18 dB, 1.59 dB, 2.09 dB on average compared with the current mainstream reconstruction algorithm SCSNet, CSNet, ISTA-Net+ respectively.
The traditional iterative-based Compressive Sensing (CS) image reconstruction algorithm is easy to integrate image prior information, but it has shortcomings such as insufficient performance and high computational complexity. The performance of the image reconstruction algorithm based on deep learning is better than the traditional reconstruction algorithm significantly, and it has lower time cost. Therefore, in order to design a deep learning image reconstruction algorithm that uses prior information more effectively, a deep compressive sensing image reconstruction network based on non-local priors is proposed. Firstly, the sparseness and non-local prior are combined to establish a compressed sensing image reconstruction model. Secondly, the model is decomposed into three sub-problems by the half quadratic splitting method. The solution of each sub-problem is carried out under the framework of deep learning. Finally, an end-to-end trainable image reconstruction model is jointly established. Simulation experiments show that the peak signal-to-noise ratio of the proposed algorithm under the tested sampling rate and dataset is improved by 0.18 dB, 1.59 dB, 2.09 dB on average compared with the current mainstream reconstruction algorithm SCSNet, CSNet, ISTA-Net+ respectively.
2023, 45(2): 664-671.
doi: 10.11999/JEIT211502
Abstract:
Compared with centralized direct position determination, distributed direct position determination algorithm has the advantages of low computational complexity and low communication cost, but it has the problem of location accuracy loss. This paper proposes a distributed direct position determination technique based on the VEPPSO-EXTRA hybrid algorithm. Firstly, based on the direct position determination algorithm of subspace fusion, a distributed optimization model is derived; Secondly, based on the idea of multi-population joint evolution, a Vector Evaluation based Parallel Particle Swarm Optimization (VEPPSO) algorithm is proposed to achieve global optimization, and the initial value of the emitter iteration is obtained; Finally, the distributed Exact First-Order Algorithnm (EXTRA) is introduced to solve the final position to reduce the accuracy loss caused by distributed computing. The experimental results show that compared with the existing distributed direct position determination algorithm, this technology can solve the problem of location accuracy loss, and its computational complexity and communication cost are lower than the corresponding centralized direct position determination algorithm.
Compared with centralized direct position determination, distributed direct position determination algorithm has the advantages of low computational complexity and low communication cost, but it has the problem of location accuracy loss. This paper proposes a distributed direct position determination technique based on the VEPPSO-EXTRA hybrid algorithm. Firstly, based on the direct position determination algorithm of subspace fusion, a distributed optimization model is derived; Secondly, based on the idea of multi-population joint evolution, a Vector Evaluation based Parallel Particle Swarm Optimization (VEPPSO) algorithm is proposed to achieve global optimization, and the initial value of the emitter iteration is obtained; Finally, the distributed Exact First-Order Algorithnm (EXTRA) is introduced to solve the final position to reduce the accuracy loss caused by distributed computing. The experimental results show that compared with the existing distributed direct position determination algorithm, this technology can solve the problem of location accuracy loss, and its computational complexity and communication cost are lower than the corresponding centralized direct position determination algorithm.
2023, 45(2): 672-679.
doi: 10.11999/JEIT211458
Abstract:
At present, the mainstream click prediction model uses the combination of linear model and deep neural network to learn the characteristic interaction between users and items, ignoring the fact that the user’s historical behavior is essentially a dynamic sequence, resulting in the inability to capture effectively the time information contained in the user’s behavior sequence. Therefore, a short video USer multi behavior Click Prediction model (USCP) based on user behavior sequence is proposed in this paper. The model sorts the user’s historical behavior in the order of interaction time, and generates the user’s historical behavior sequence. Based on the DeepFM model, the word embedding model word2vec is introduced to learn adaptively the user’s dynamic interest according to the user’s historical behavior sequence and capture effectively the changes of user interest. A comparative experiment is carried out on the desensitization data set published on a short video platform. The evaluation index adopts GAUC (Group AUC). The results show that the performance of this model is better than other models.
At present, the mainstream click prediction model uses the combination of linear model and deep neural network to learn the characteristic interaction between users and items, ignoring the fact that the user’s historical behavior is essentially a dynamic sequence, resulting in the inability to capture effectively the time information contained in the user’s behavior sequence. Therefore, a short video USer multi behavior Click Prediction model (USCP) based on user behavior sequence is proposed in this paper. The model sorts the user’s historical behavior in the order of interaction time, and generates the user’s historical behavior sequence. Based on the DeepFM model, the word embedding model word2vec is introduced to learn adaptively the user’s dynamic interest according to the user’s historical behavior sequence and capture effectively the changes of user interest. A comparative experiment is carried out on the desensitization data set published on a short video platform. The evaluation index adopts GAUC (Group AUC). The results show that the performance of this model is better than other models.
Research on Effect Index of Closed-loop Deep Brain Stimulation in Parkinson's Disease Based on Model
2023, 45(2): 680-688.
doi: 10.11999/JEIT211516
Abstract:
With the continuous improvement of the aging population, Parkinson’s Disease (PD) that is more prevalent in middle-aged and elderly people will put heavy burden on society. However, the stimulation effect evaluation index of model-based Deep Brain Stimulation (DBS) for PD is single and not intuitive. Therefore, a new index the Similar to Unified Parkinson Disease Rating Scale (UPDRS) Estimates (SUE) is proposed. The feasibility of the computational model and the closed-loop DBS algorithm is firstly verified according to the power changes of the β band (13～35 Hz). The distribution of β bursts in time domain is statistically analyzed, and is dichotomized into long and short bursts, then SUE is proposed. The experimental results show that SUE has a strong correlation with the duration of β bursts, the change of UPDRS under stimulated state and unstimulated state is well simulated, and a foundation for the future model-based closed-loop DBS research is laid.
With the continuous improvement of the aging population, Parkinson’s Disease (PD) that is more prevalent in middle-aged and elderly people will put heavy burden on society. However, the stimulation effect evaluation index of model-based Deep Brain Stimulation (DBS) for PD is single and not intuitive. Therefore, a new index the Similar to Unified Parkinson Disease Rating Scale (UPDRS) Estimates (SUE) is proposed. The feasibility of the computational model and the closed-loop DBS algorithm is firstly verified according to the power changes of the β band (13～35 Hz). The distribution of β bursts in time domain is statistically analyzed, and is dichotomized into long and short bursts, then SUE is proposed. The experimental results show that SUE has a strong correlation with the duration of β bursts, the change of UPDRS under stimulated state and unstimulated state is well simulated, and a foundation for the future model-based closed-loop DBS research is laid.
2023, 45(2): 689-697.
doi: 10.11999/JEIT211431
Abstract:
As a powerful text generation model, the Variational AutoEncoder(VAE) has attracted more and more attention. However, in the process of optimization, the variational auto-encoder tends to ignore the potential variables and degenerates into an auto-encoder, called a posteriori collapse. A new variational auto-encoder model is proposed in this paper, called Hierarchical Status Regularisation Variational AutoEncoder (HSR-VAE), which can effectively alleviate the problem of posterior collapse through hierarchical coding and state regularization and has better model performance than the baseline model. On this basis, based on the nanometer memristor, the model is combined with the memristor Recurrent Neural Network (RNN). A hardware implementation scheme based on a memristor recurrent neural network is proposed to realize the hardware acceleration of the model, which called Hierarchical Variational AutoEncoder Memristor Neural Networks (HVAE-MHN). Computer simulation experiments and result analysis verify the validity and superiority of the proposed model.
As a powerful text generation model, the Variational AutoEncoder(VAE) has attracted more and more attention. However, in the process of optimization, the variational auto-encoder tends to ignore the potential variables and degenerates into an auto-encoder, called a posteriori collapse. A new variational auto-encoder model is proposed in this paper, called Hierarchical Status Regularisation Variational AutoEncoder (HSR-VAE), which can effectively alleviate the problem of posterior collapse through hierarchical coding and state regularization and has better model performance than the baseline model. On this basis, based on the nanometer memristor, the model is combined with the memristor Recurrent Neural Network (RNN). A hardware implementation scheme based on a memristor recurrent neural network is proposed to realize the hardware acceleration of the model, which called Hierarchical Variational AutoEncoder Memristor Neural Networks (HVAE-MHN). Computer simulation experiments and result analysis verify the validity and superiority of the proposed model.
2023, 45(2): 698-706.
doi: 10.11999/JEIT211567
Abstract:
TypeNet is a Siamese network model based on two-layer Long-Short Term Memory (LSTM) branch structure. It has achieved good results in the classification of free-text keystroke event sequences, but lacks interpretation. Therefore, the TypeNet model is transformed, and a Siamese network TypeNet II based on a single-layer LSTM branch structure is proposed. A multi-layer perceptron is used to measure the similarity of two feature sequences reflected by the absolute value of the difference between the output embeddings of the two branches. After the model training, the multi-layer perceptron is simulated by a multivariate binomial expression. Based on the obtained multivariate binomial expression, the classification judgment of the model can be explained. The experimental results show that the classification effect of the TypeNet II model exceeds the existing TypeNet model. The results of multivariate binomial regression are generalized, and there is a nonlinear relationship between the absolute value of the difference of the embeddings and the similarity measure.
TypeNet is a Siamese network model based on two-layer Long-Short Term Memory (LSTM) branch structure. It has achieved good results in the classification of free-text keystroke event sequences, but lacks interpretation. Therefore, the TypeNet model is transformed, and a Siamese network TypeNet II based on a single-layer LSTM branch structure is proposed. A multi-layer perceptron is used to measure the similarity of two feature sequences reflected by the absolute value of the difference between the output embeddings of the two branches. After the model training, the multi-layer perceptron is simulated by a multivariate binomial expression. Based on the obtained multivariate binomial expression, the classification judgment of the model can be explained. The experimental results show that the classification effect of the TypeNet II model exceeds the existing TypeNet model. The results of multivariate binomial regression are generalized, and there is a nonlinear relationship between the absolute value of the difference of the embeddings and the similarity measure.
2023, 45(2): 707-717.
doi: 10.11999/JEIT211306
Abstract:
To deal with automatic threshold selection issue in non-modal, unimodal, bimodal or multimodal situations within a unified framework, a Tsallis Entropy thresholding segmentation method based on Multi-scale and multi-direction Gabor transform (MGTE) is proposed. The multi-scale product image is first obtained by the Gabor transform and then the inner and outer contour images are used to reconstruct the one-dimensional histogram from the multi-scale product image. Based on the reconstruction of the one-dimensional histogram, the Tsallis entropy calculation model is utilized to select 4 thresholds by maximizing Tsallis entropy in 4 different directions, and finally the weighted sum of the 4 thresholds is used as the final threshold. The proposed method is compared with 5 segmentation methods on 4 synthetic images and 40 real-world images. The results show that the proposed method has no advantage in computational efficiency, but its adaptability and segmentation accuracy are significantly improved.
To deal with automatic threshold selection issue in non-modal, unimodal, bimodal or multimodal situations within a unified framework, a Tsallis Entropy thresholding segmentation method based on Multi-scale and multi-direction Gabor transform (MGTE) is proposed. The multi-scale product image is first obtained by the Gabor transform and then the inner and outer contour images are used to reconstruct the one-dimensional histogram from the multi-scale product image. Based on the reconstruction of the one-dimensional histogram, the Tsallis entropy calculation model is utilized to select 4 thresholds by maximizing Tsallis entropy in 4 different directions, and finally the weighted sum of the 4 thresholds is used as the final threshold. The proposed method is compared with 5 segmentation methods on 4 synthetic images and 40 real-world images. The results show that the proposed method has no advantage in computational efficiency, but its adaptability and segmentation accuracy are significantly improved.
2023, 45(2): 718-724.
doi: 10.11999/JEIT211304
Abstract:
TeraHertz (THz) with high frequency and large bandwidth is an advantageous potential wireless spectrum resource in Sixth Generation (6G) mobile communication. However, the nonlinear distortion of THz devices limits the power conversion efficiency and the communication transmission distance. If traditional Digital Pre-Distortion (DPD) technology is used for non-linear correction, the sampling rate of the Digital-to-Analog Converter (DAC) and Analog-to-Digital Converter (ADC) is usually required to reach 5 times the signal bandwidth, which is difficult to apply to the THz frequency band. Therefore, in this paper, a digital correction method is proposed to correct the nonlinearity of the THz transmitter using the low-rate DAC and ADC. This method is mainly divided into three steps: Firstly, the observation data obtained by the low sampling rate ADC is used, and the high sampling rate observation signal with limited bandwidth is recovered by up-sampling. At this time, the signal sampling rate is 5 times the signal bandwidth, which can effectively characterize the 5th order nonlinear distortion; Then the DPD model with limited bandwidth is established to extract the DPD correction coefficient; Finally, the corrected signal is down-sampled to the DAC to correct the nonlinear distortion of the transmitter channel. The simulation results show that when DAC and ADC work at the sampling rate of 1.25 times the baseband signal rate, for a 64-QAM modulated signal, the Error Vector Magnitude (EVM) can be reduced from 8.46% to 2.27%. Therefore, modulation schemes with higher spectrum efficiency can be adopted in THz communications.
TeraHertz (THz) with high frequency and large bandwidth is an advantageous potential wireless spectrum resource in Sixth Generation (6G) mobile communication. However, the nonlinear distortion of THz devices limits the power conversion efficiency and the communication transmission distance. If traditional Digital Pre-Distortion (DPD) technology is used for non-linear correction, the sampling rate of the Digital-to-Analog Converter (DAC) and Analog-to-Digital Converter (ADC) is usually required to reach 5 times the signal bandwidth, which is difficult to apply to the THz frequency band. Therefore, in this paper, a digital correction method is proposed to correct the nonlinearity of the THz transmitter using the low-rate DAC and ADC. This method is mainly divided into three steps: Firstly, the observation data obtained by the low sampling rate ADC is used, and the high sampling rate observation signal with limited bandwidth is recovered by up-sampling. At this time, the signal sampling rate is 5 times the signal bandwidth, which can effectively characterize the 5th order nonlinear distortion; Then the DPD model with limited bandwidth is established to extract the DPD correction coefficient; Finally, the corrected signal is down-sampled to the DAC to correct the nonlinear distortion of the transmitter channel. The simulation results show that when DAC and ADC work at the sampling rate of 1.25 times the baseband signal rate, for a 64-QAM modulated signal, the Error Vector Magnitude (EVM) can be reduced from 8.46% to 2.27%. Therefore, modulation schemes with higher spectrum efficiency can be adopted in THz communications.
2023, 45(2): 725-733.
doi: 10.11999/JEIT211317
Abstract:
At present, most of the researches on the memristor simulators are flux-controlled, there are few researches on the charge-controlled memristor simulator, and the hyperbolic function simulator is seldom mentioned. Therefore, a general-purpose simulator of charge-controlled memristor based on hyperbolic function is proposed. The simulator realizes the conversion between voltage and current signals in the circuit through the voltage-current mutual conversion circuit, and calculates the generated signals through the corresponding module in the circuit, and finally obtains the universal hyperbolic charge-controlled memristor model. The simulator can realize the charge-controlled memristor corresponding to hyperbolic sine, hyperbolic cosine and hyperbolic tangent function. The equivalent circuit of the general-purpose hyperbolic function charge-controlled memristor simulator is mainly composed of operational amplifier, resistor, capacitor, triode and other basic components. By analyzing the volt-ampere characteristic curves of the simulator at different amplitudes and different frequencies, it is concluded that the simulator conforms to the basic characteristics of memory devices. The model of hyperbolic charge-controlled memristor presented in this paper has a certain reference significance for the development of memristor model.
At present, most of the researches on the memristor simulators are flux-controlled, there are few researches on the charge-controlled memristor simulator, and the hyperbolic function simulator is seldom mentioned. Therefore, a general-purpose simulator of charge-controlled memristor based on hyperbolic function is proposed. The simulator realizes the conversion between voltage and current signals in the circuit through the voltage-current mutual conversion circuit, and calculates the generated signals through the corresponding module in the circuit, and finally obtains the universal hyperbolic charge-controlled memristor model. The simulator can realize the charge-controlled memristor corresponding to hyperbolic sine, hyperbolic cosine and hyperbolic tangent function. The equivalent circuit of the general-purpose hyperbolic function charge-controlled memristor simulator is mainly composed of operational amplifier, resistor, capacitor, triode and other basic components. By analyzing the volt-ampere characteristic curves of the simulator at different amplitudes and different frequencies, it is concluded that the simulator conforms to the basic characteristics of memory devices. The model of hyperbolic charge-controlled memristor presented in this paper has a certain reference significance for the development of memristor model.
2023, 45(2): 734-745.
doi: 10.11999/JEIT211474
Abstract:
Near-field source localization plays an important role in the radar, sonar and communications. The near-field source localization methods and the resolving ambiguity methods via uniform circular array are systematically introduced in this paper. On this basis, the fast and accurate algorithms for three-dimensional position parameter (azimuth angle, elevation angle and range) estimation of near-field Linear Frequency Modulated (LFM) signal are further introduced from time domain, frequency domain, and fractional Fourier domain. Finally, the following research ideas are proposed from the aspects of coherent source and mixed source localization.
Near-field source localization plays an important role in the radar, sonar and communications. The near-field source localization methods and the resolving ambiguity methods via uniform circular array are systematically introduced in this paper. On this basis, the fast and accurate algorithms for three-dimensional position parameter (azimuth angle, elevation angle and range) estimation of near-field Linear Frequency Modulated (LFM) signal are further introduced from time domain, frequency domain, and fractional Fourier domain. Finally, the following research ideas are proposed from the aspects of coherent source and mixed source localization.
2023, 45(2): 746-756.
doi: 10.11999/JEIT221202
Abstract:
Data, algorithms, and hash rates are the three thrust forces for developing artificial intelligence. Considering the urgent demand for research on the intelligent association algorithm and the difficulty of obtaining track data from multi-radar collaborative observation and addressing the problem of missing track association dataset, a Multi-source Track Association Dataset (MTAD) is constructed in this study. MTAD is based on automatic identification system trajectory data after processing grid division, automatic interruption, and error adding. The dataset includes two parts, namely, the training dataset and the test dataset, with more than 1 million tracks. The train and test datasets contain 5000 and 1000 scene samples, respectively. Each scene sample consists of several to hundreds of tracks, covering various movement patterns, target types, and duration times. In addition, the constructed MTAD is further visualized and analyzed, and the characteristics of tracks in each grid are studied in detail, demonstrating the richness, rationality, and effectiveness of the MTAD. The indicators and baseline results of the association are obtained. This dataset has already been used as a dedicated dataset for the Navy’s “Golden Dolphin” Cup competition.
Data, algorithms, and hash rates are the three thrust forces for developing artificial intelligence. Considering the urgent demand for research on the intelligent association algorithm and the difficulty of obtaining track data from multi-radar collaborative observation and addressing the problem of missing track association dataset, a Multi-source Track Association Dataset (MTAD) is constructed in this study. MTAD is based on automatic identification system trajectory data after processing grid division, automatic interruption, and error adding. The dataset includes two parts, namely, the training dataset and the test dataset, with more than 1 million tracks. The train and test datasets contain 5000 and 1000 scene samples, respectively. Each scene sample consists of several to hundreds of tracks, covering various movement patterns, target types, and duration times. In addition, the constructed MTAD is further visualized and analyzed, and the characteristics of tracks in each grid are studied in detail, demonstrating the richness, rationality, and effectiveness of the MTAD. The indicators and baseline results of the association are obtained. This dataset has already been used as a dedicated dataset for the Navy’s “Golden Dolphin” Cup competition.
2023, 45(2): 757-764.
Abstract:
Classified by general program, young science fund, fund for less developed regions, key program, excellent young science fund and national science fund for distinguished young scholars under the grant application code F01, an overview of proposal application, peer review and funding of Information Acquisition and Processing in 2022 is presented in this paper. In order to provide some insight and perspective for scientific researchers, the data are analyzed from different aspects and the main reform measures in this year, such as category-specific review and the evaluation mechanism featuring “Responsibility + Credibility + Contribution”, are introduced. In the end, some development trends in the field of Information Acquisition and Processing during the 14th five-year plan period are prospected.
Classified by general program, young science fund, fund for less developed regions, key program, excellent young science fund and national science fund for distinguished young scholars under the grant application code F01, an overview of proposal application, peer review and funding of Information Acquisition and Processing in 2022 is presented in this paper. In order to provide some insight and perspective for scientific researchers, the data are analyzed from different aspects and the main reform measures in this year, such as category-specific review and the evaluation mechanism featuring “Responsibility + Credibility + Contribution”, are introduced. In the end, some development trends in the field of Information Acquisition and Processing during the 14th five-year plan period are prospected.
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