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2022 Vol. 44, No. 3
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2022, 44(3): 781-789.
doi: 10.11999/JEIT210789
Abstract:
Although the application of the fifth-Generation (5G) mobile communication has brought tremendous innovations to the daily life of human beings, e.g., autonomous vehicles and internet of everything, the upcoming huger data requirement leads to the emergence of the sixth-Generation (6G) mobile communication. Compared to 5G, the transmission rate, time delay, and wireless coverage need to be improved significantly. Thus, in this paper the applications of Unmanned Aerial Vehicles (UAVs) to the ubiquitous, intelligent and coupling 6G network are surveyed. First, the utilization of UAVs in the framework of space-air-ground-sea integrated network is demonstrated, and the roles and functions of UAVs in different scenarios are emphasized, e.g., the swarm base stations, the deployment for holographic projection, the long-distance relaying and the data collection. Then, the potential 6G key techniques of terahertz, ultra-massive multiple-input and multiple-output, endogenous artificial intelligence, Intelligent Reflecting Surface (IRS), intelligent edge computing, blockchain and integrated sensing and communication for UAV communications are investigated. Finally, the future challenges of UAV communications for 6G, including the limited duration, integration of networks, compatibility of IRS, development of THz communications, and user security are discussed.
Although the application of the fifth-Generation (5G) mobile communication has brought tremendous innovations to the daily life of human beings, e.g., autonomous vehicles and internet of everything, the upcoming huger data requirement leads to the emergence of the sixth-Generation (6G) mobile communication. Compared to 5G, the transmission rate, time delay, and wireless coverage need to be improved significantly. Thus, in this paper the applications of Unmanned Aerial Vehicles (UAVs) to the ubiquitous, intelligent and coupling 6G network are surveyed. First, the utilization of UAVs in the framework of space-air-ground-sea integrated network is demonstrated, and the roles and functions of UAVs in different scenarios are emphasized, e.g., the swarm base stations, the deployment for holographic projection, the long-distance relaying and the data collection. Then, the potential 6G key techniques of terahertz, ultra-massive multiple-input and multiple-output, endogenous artificial intelligence, Intelligent Reflecting Surface (IRS), intelligent edge computing, blockchain and integrated sensing and communication for UAV communications are investigated. Finally, the future challenges of UAV communications for 6G, including the limited duration, integration of networks, compatibility of IRS, development of THz communications, and user security are discussed.
2022, 44(3): 790-802.
doi: 10.11999/JEIT210819
Abstract:
With the iterative update of mobile communication technology, Vehicular Ad-hoc NETworks (VANETs) and Flying Ad-hoc NETworks (FANETs) have become significant parts of the communication network, and the Medium Access Control (MAC) protocol is one of the core research contents of the future development of Mobile Ad-hoc NETworks (MANETs). Security control and user service are the two principal types of message in Ad-hoc networks, where their different Quality of Service (QoS) requirements bring severe challenges to the design of MAC mechanism. In this paper, VANETs and FANETs are mainly taken into consideration. According to their network characteristics and different optimization objectives, the MAC protocols used in them are analyze and summarized, while the future research directions are discussed and prospected.
With the iterative update of mobile communication technology, Vehicular Ad-hoc NETworks (VANETs) and Flying Ad-hoc NETworks (FANETs) have become significant parts of the communication network, and the Medium Access Control (MAC) protocol is one of the core research contents of the future development of Mobile Ad-hoc NETworks (MANETs). Security control and user service are the two principal types of message in Ad-hoc networks, where their different Quality of Service (QoS) requirements bring severe challenges to the design of MAC mechanism. In this paper, VANETs and FANETs are mainly taken into consideration. According to their network characteristics and different optimization objectives, the MAC protocols used in them are analyze and summarized, while the future research directions are discussed and prospected.
2022, 44(3): 803-814.
doi: 10.11999/JEIT211509
Abstract:
The high mobility of Unmanned Aerial Vehicles (UAVs) can be utilized to meet requirements of the next-generation communication networks such as high coverage and low latency, while due to the broadcast nature of wireless channels and the increasing number of nodes, the problem of secure transmission also needs to be urgently addressed. Since UAVs are resource-constrained aerial platforms, upper-layer encryption techniques can hardly play an equally effective role in UAV communication networks. The essence of physical layer security is to design artificially the communication channel so as to maximize the difference between the legitimate and eavesdropping channel, and the application of physical layer security technology to UAV communication networks can assist to achieve a compromise between confidential transmission and energy efficiency. This paper reviews the current researches on physical layer security transmission technology of UAV communication networks. Specifically, typical physical layer security transmission technologies are firstly introduced in scenarios, and then the challenges in their applications into UAV communication networks are analyzed. Finally, new scenarios, technologies, and methods for the development of physical layer security transmission technologies in UAV communication networks are prospected to provide a new perspective for the research on physical layer security transmission technology of UAV communication networks.
The high mobility of Unmanned Aerial Vehicles (UAVs) can be utilized to meet requirements of the next-generation communication networks such as high coverage and low latency, while due to the broadcast nature of wireless channels and the increasing number of nodes, the problem of secure transmission also needs to be urgently addressed. Since UAVs are resource-constrained aerial platforms, upper-layer encryption techniques can hardly play an equally effective role in UAV communication networks. The essence of physical layer security is to design artificially the communication channel so as to maximize the difference between the legitimate and eavesdropping channel, and the application of physical layer security technology to UAV communication networks can assist to achieve a compromise between confidential transmission and energy efficiency. This paper reviews the current researches on physical layer security transmission technology of UAV communication networks. Specifically, typical physical layer security transmission technologies are firstly introduced in scenarios, and then the challenges in their applications into UAV communication networks are analyzed. Finally, new scenarios, technologies, and methods for the development of physical layer security transmission technologies in UAV communication networks are prospected to provide a new perspective for the research on physical layer security transmission technology of UAV communication networks.
2022, 44(3): 815-824.
doi: 10.11999/JEIT211140
Abstract:
With the development of wireless communication and Unmanned Aerial Vehicle (UAV) technology, the establishment of a large-scale UAV cloud covering and connecting various wireless terminals in a wide area using the advantages of mobility, stability and wide coverage has become an important development direction for future 6G wireless communication networks. How to quickly and accurately plan the optimal path in the complex network topology of the UAV cloud has become an urgent problem to be solved. Therefore, by using the gradient principle in the gravitational field, a new dynamic network topology model designed to be applicable to multiple routing schemes is designed, and the calculation and selection of routing paths under complex topological networks is realized based on this model. This model uses the characteristics of gradient itself in order to achieve path optimization for the communication needs of high-density and high-coverage UAV clouds that may appear in future 6G applications. Simulation results show that the topology model and routing methods proposed in this paper outperform many routing schemes currently applied and studied in terms of link communication quality and average energy consumption.
With the development of wireless communication and Unmanned Aerial Vehicle (UAV) technology, the establishment of a large-scale UAV cloud covering and connecting various wireless terminals in a wide area using the advantages of mobility, stability and wide coverage has become an important development direction for future 6G wireless communication networks. How to quickly and accurately plan the optimal path in the complex network topology of the UAV cloud has become an urgent problem to be solved. Therefore, by using the gradient principle in the gravitational field, a new dynamic network topology model designed to be applicable to multiple routing schemes is designed, and the calculation and selection of routing paths under complex topological networks is realized based on this model. This model uses the characteristics of gradient itself in order to achieve path optimization for the communication needs of high-density and high-coverage UAV clouds that may appear in future 6G applications. Simulation results show that the topology model and routing methods proposed in this paper outperform many routing schemes currently applied and studied in terms of link communication quality and average energy consumption.
2022, 44(3): 825-834.
doi: 10.11999/JEIT211260
Abstract:
In view of the problems that the existing communication interference intelligent recognition methods have low recognition accuracy under the small samples condition and the under-fitting of the network model, an intelligent communication interference recognition method based on twin network in the air-to-ground collaboration scenario is proposed to form the air and ground layout ability. Firstly, the time-frequency diagram, fractional Fourier transform and constellation diagram of the received communication interference signals are extracted as network inputs in the air-to-ground collaboration communication interference cognitive architecture between unmanned air vehicles and ground equipment. Secondly, the network structure based on densely connected convolutional networks is built, and the twin network with dual input weight sharing is designed. Finally, the twin network is trained with random samples, and the benchmark communication interference type feature library is constructed through the twin unilateral network, so as to realize the communication interference intelligent identification. The proposed method evaluates the similarity of samples by measuring the feature distance between two samples, and expands the number of training samples and trains the Siamese network model through the similarity measurement. Simulation results show that the proposed method can effectively realize the communication interference recognition under the support of small data sets, and the recognition performance is significantly improved compared with the existing intelligent recognition methods.
In view of the problems that the existing communication interference intelligent recognition methods have low recognition accuracy under the small samples condition and the under-fitting of the network model, an intelligent communication interference recognition method based on twin network in the air-to-ground collaboration scenario is proposed to form the air and ground layout ability. Firstly, the time-frequency diagram, fractional Fourier transform and constellation diagram of the received communication interference signals are extracted as network inputs in the air-to-ground collaboration communication interference cognitive architecture between unmanned air vehicles and ground equipment. Secondly, the network structure based on densely connected convolutional networks is built, and the twin network with dual input weight sharing is designed. Finally, the twin network is trained with random samples, and the benchmark communication interference type feature library is constructed through the twin unilateral network, so as to realize the communication interference intelligent identification. The proposed method evaluates the similarity of samples by measuring the feature distance between two samples, and expands the number of training samples and trains the Siamese network model through the similarity measurement. Simulation results show that the proposed method can effectively realize the communication interference recognition under the support of small data sets, and the recognition performance is significantly improved compared with the existing intelligent recognition methods.
2022, 44(3): 835-843.
doi: 10.11999/JEIT211241
Abstract:
Distributed Access Points (AP) in the cell-free massive Multiple Input Multiple Output (MIMO) networks serve multiple users at the same time, which can achieve large-capacity transmission of virtual MIMO in a larger area. Unmanned Aerial Vehicle (UAV) assisted communication can provide coverage enhancement for hotspots or edge users in this area. In order to improve the spectrum efficiency and reduce the feedback overhead, a joint resource scheduling scheme that includes AP power allocation, UAV service zone selection and user scheduling is proposed in this paper. Firstly, the AP power allocation and the UAV service zone selection problems are jointly modeled as a Double-Action Markov Decision Process (DAMDP). Then, a Deep Reinforcement Learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN) is proposed. Furthermore, the user scheduling problem is formulated as a 0-1 optimization problem and solved by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme exhibits a higher spectrum efficiency than existing schemes.
Distributed Access Points (AP) in the cell-free massive Multiple Input Multiple Output (MIMO) networks serve multiple users at the same time, which can achieve large-capacity transmission of virtual MIMO in a larger area. Unmanned Aerial Vehicle (UAV) assisted communication can provide coverage enhancement for hotspots or edge users in this area. In order to improve the spectrum efficiency and reduce the feedback overhead, a joint resource scheduling scheme that includes AP power allocation, UAV service zone selection and user scheduling is proposed in this paper. Firstly, the AP power allocation and the UAV service zone selection problems are jointly modeled as a Double-Action Markov Decision Process (DAMDP). Then, a Deep Reinforcement Learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN) is proposed. Furthermore, the user scheduling problem is formulated as a 0-1 optimization problem and solved by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme exhibits a higher spectrum efficiency than existing schemes.
2022, 44(3): 844-851.
doi: 10.11999/JEIT211194
Abstract:
The combination of Unmanned Aerial Vehicle (UAV) and millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) system can provide high data rate. However, deployment location of UAV and beamforming design affect directly the throughput of wireless communication system. To realize multi-user simultaneous access communication, beam space precoding technique based on Discrete Lens Array (DLA) structure is proposed in this paper, and an optimization scheme of joint UAV flight altitude, beam selection and hybrid precoding is constructed. To solve this highly non-convex problem that involves a stochastic objective function, this paper exploits the minimization weighted minimum mean square error method, transforms the problem into a series of simple approximation problems and then develops a Penalized Dual Decomposition (PDD) algorithm to solve the peoblem. Numerical simulations results show that the proposed scheme achieve near optimal achievable sum rate performance and close to full digital beamforming.
The combination of Unmanned Aerial Vehicle (UAV) and millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) system can provide high data rate. However, deployment location of UAV and beamforming design affect directly the throughput of wireless communication system. To realize multi-user simultaneous access communication, beam space precoding technique based on Discrete Lens Array (DLA) structure is proposed in this paper, and an optimization scheme of joint UAV flight altitude, beam selection and hybrid precoding is constructed. To solve this highly non-convex problem that involves a stochastic objective function, this paper exploits the minimization weighted minimum mean square error method, transforms the problem into a series of simple approximation problems and then develops a Penalized Dual Decomposition (PDD) algorithm to solve the peoblem. Numerical simulations results show that the proposed scheme achieve near optimal achievable sum rate performance and close to full digital beamforming.
2022, 44(3): 852-859.
doi: 10.11999/JEIT211280
Abstract:
In Unmanned Aerial Vehicle (UAV)-enabled wireless network, the trajectory design of UAV can effectively improve the system performance. However, due to the high complexity in 3D scenario, UAV trajectory design is still an open research problem, where effective solutions are still missing. For UAV 3D trajectory design problems in general Wireless Power Transfer (WPT) system, this paper proposes a solution to obtain effective 3D trajectory based on the Successive Hovering and Flying (SHF) structure in convex space.
In Unmanned Aerial Vehicle (UAV)-enabled wireless network, the trajectory design of UAV can effectively improve the system performance. However, due to the high complexity in 3D scenario, UAV trajectory design is still an open research problem, where effective solutions are still missing. For UAV 3D trajectory design problems in general Wireless Power Transfer (WPT) system, this paper proposes a solution to obtain effective 3D trajectory based on the Successive Hovering and Flying (SHF) structure in convex space.
2022, 44(3): 860-870.
doi: 10.11999/JEIT210992
Abstract:
A deployment and networking methods of Unmanned Aerial Vehicle (UAV) swarm based on game theory in the jamming environments is investigated in this paper. Firstly, a Congestion-game based UAV swarm Deployment algorithm (CUD）is proposed. Each UAV can autonomously optimize its position through limited interaction with adjacent UAVs to increase the amount of collected data and enhance the anti-jamming capabilities. Secondly, a UAV Swarm Anti-jamming Coalition Formation algorithm (USACF) is proposed, which enables the UAV swarm to form dynamic sub-networks in a distributed way under the threat of hostile jamming, thus improving the transmission performance and enhancing the robustness and reliability of the UAV networks. Furthermore, it is proved theoretically that the proposed game model can achieve a stable Nash equilibrium with the aid of exact potential game theory. Finally, simulation results verify that the proposed algorithms have obvious performance improvement compared with the conventional algorithms.
A deployment and networking methods of Unmanned Aerial Vehicle (UAV) swarm based on game theory in the jamming environments is investigated in this paper. Firstly, a Congestion-game based UAV swarm Deployment algorithm (CUD）is proposed. Each UAV can autonomously optimize its position through limited interaction with adjacent UAVs to increase the amount of collected data and enhance the anti-jamming capabilities. Secondly, a UAV Swarm Anti-jamming Coalition Formation algorithm (USACF) is proposed, which enables the UAV swarm to form dynamic sub-networks in a distributed way under the threat of hostile jamming, thus improving the transmission performance and enhancing the robustness and reliability of the UAV networks. Furthermore, it is proved theoretically that the proposed game model can achieve a stable Nash equilibrium with the aid of exact potential game theory. Finally, simulation results verify that the proposed algorithms have obvious performance improvement compared with the conventional algorithms.
2022, 44(3): 871-880.
doi: 10.11999/JEIT220024
Abstract:
The UAV-assisted communication network can supplement the existing wireless communication network and improve the performance of the communication system and the coverage service range, but the improvement of the uplink transmission communication rate in the process of UAV-assisted communication still faces huge challenges. Aiming at how to improve the uplink transmission communication rate of UAV-assisted communication network, this paper proposes a UAV-assisted communication network uplink transmission technology based on joint beamforming. First, Bernoulli particle filtering is performed on the received signal strength value of the user node, combined with the UAV motion model to complete the UAV positioning, and further, the user node uses jointly the distributed beamforming algorithm to send signals to the UAV direction to complete the uplink transmission communication. Compared with the non-orthogonal multiple access algorithm and the traditional omnidirectional transmission algorithm, the experimental results show that the proposed method improves significantly the signal-to-noise ratio and communication rate of the UAV received signal, and ensures the uplink communication of the UAV. It provides a potential solution to guarantee the communication performance for the uplink transmission of the UAV-assisted communication network in the future.
The UAV-assisted communication network can supplement the existing wireless communication network and improve the performance of the communication system and the coverage service range, but the improvement of the uplink transmission communication rate in the process of UAV-assisted communication still faces huge challenges. Aiming at how to improve the uplink transmission communication rate of UAV-assisted communication network, this paper proposes a UAV-assisted communication network uplink transmission technology based on joint beamforming. First, Bernoulli particle filtering is performed on the received signal strength value of the user node, combined with the UAV motion model to complete the UAV positioning, and further, the user node uses jointly the distributed beamforming algorithm to send signals to the UAV direction to complete the uplink transmission communication. Compared with the non-orthogonal multiple access algorithm and the traditional omnidirectional transmission algorithm, the experimental results show that the proposed method improves significantly the signal-to-noise ratio and communication rate of the UAV received signal, and ensures the uplink communication of the UAV. It provides a potential solution to guarantee the communication performance for the uplink transmission of the UAV-assisted communication network in the future.
2022, 44(3): 881-889.
doi: 10.11999/JEIT211360
Abstract:
The rapid growth of data and the computing limitations of devices have spawned Mobile Edge Computing (MEC) solutions in Internet of Things. Among them, the high maneuverability, easy deployment and low cost of the Unmanned Aerial Vehicle (UAV) swarm and Multiple Input Multiple Output (MIMO) technology can enhance the transmission capacity and shorten the transmission delay in the MEC network. In this paper, the maximum total delay of the system are minimized by jointly optimizing the UAV trajectory, ground users’ratio of data offloaded, assisted UAV’s ratio of data offloaded and assisted UAV’sratio of data allocation in the multi-UAVs MIMO-MEC system, in which successive convex optimization technology and block coordinate descent method are used to solve the non-convex problem. The factors affecting the system delay are discussed, and the effectiveness and convergence of the algorithm is verified in the simulation results.
The rapid growth of data and the computing limitations of devices have spawned Mobile Edge Computing (MEC) solutions in Internet of Things. Among them, the high maneuverability, easy deployment and low cost of the Unmanned Aerial Vehicle (UAV) swarm and Multiple Input Multiple Output (MIMO) technology can enhance the transmission capacity and shorten the transmission delay in the MEC network. In this paper, the maximum total delay of the system are minimized by jointly optimizing the UAV trajectory, ground users’ratio of data offloaded, assisted UAV’s ratio of data offloaded and assisted UAV’sratio of data allocation in the multi-UAVs MIMO-MEC system, in which successive convex optimization technology and block coordinate descent method are used to solve the non-convex problem. The factors affecting the system delay are discussed, and the effectiveness and convergence of the algorithm is verified in the simulation results.
2022, 44(3): 890-898.
doi: 10.11999/JEIT211305
Abstract:
Considering an ocean data collection scenario, to improve data collection efficiency, this paper proposes a joint data collection approach for UAVs and platform. The platform releases UAVs to collect the data and sails to the designated places to recover them, while the UAVs are responsible for the data collection task. To minimize the overall working time of the UAVs and the platform, this paper introduces the Successive-Hover-and-Fly (SHF) structure to achieve a low-complexity joint trajectory optimization on basis of task allocation of the UAV cluster. The formulated problem is difficult to be solved due to the non-convexity, which is constrained by the demanded upload data amount and a maximum UAV speed. To address this problem, an efficient successive convex approximation technique iterative algorithm is proposed to obtain a sub-optimal solution, which is validated by simulation.
Considering an ocean data collection scenario, to improve data collection efficiency, this paper proposes a joint data collection approach for UAVs and platform. The platform releases UAVs to collect the data and sails to the designated places to recover them, while the UAVs are responsible for the data collection task. To minimize the overall working time of the UAVs and the platform, this paper introduces the Successive-Hover-and-Fly (SHF) structure to achieve a low-complexity joint trajectory optimization on basis of task allocation of the UAV cluster. The formulated problem is difficult to be solved due to the non-convexity, which is constrained by the demanded upload data amount and a maximum UAV speed. To address this problem, an efficient successive convex approximation technique iterative algorithm is proposed to obtain a sub-optimal solution, which is validated by simulation.
2022, 44(3): 899-905.
doi: 10.11999/JEIT211314
Abstract:
Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) can provide energy and task calculation for wireless device, improving effectively the energy efficiency. In this paper, an energy consumption optimization method in UAV wireless power transfer based mobile edge computing system is proposed. In the proposed method, the total energy consumption of the system is minimized through joint optimization of Energy Harvesting (EH) time, user transmit power and offloading strategy. By utilizing Block Coordinate Descent (BCD) method, the optimization problem is divided into two subproblems. The optimal EH time, user transmit power and offloading strategy are obtained through alternate iteration. Simulation results show that the proposed energy consumption optimization method outperforms other benchmark schemes, in which the energy consumption of the system can be significantly reduced.
Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) can provide energy and task calculation for wireless device, improving effectively the energy efficiency. In this paper, an energy consumption optimization method in UAV wireless power transfer based mobile edge computing system is proposed. In the proposed method, the total energy consumption of the system is minimized through joint optimization of Energy Harvesting (EH) time, user transmit power and offloading strategy. By utilizing Block Coordinate Descent (BCD) method, the optimization problem is divided into two subproblems. The optimal EH time, user transmit power and offloading strategy are obtained through alternate iteration. Simulation results show that the proposed energy consumption optimization method outperforms other benchmark schemes, in which the energy consumption of the system can be significantly reduced.
2022, 44(3): 906-914.
doi: 10.11999/JEIT211328
Abstract:
At present, Unmanned Aerial Vehicle (UAV) positioning technology relies mainly on the represented Global Positioning System (GPS). However, it is difficult to locate where GPS signals are missing in the room. On the other hand, the traditional indoor positioning technology uses mainly Bluetooth, WiFi, base station positioning and other methods to merge into a set of positioning system. However, this kind of methods are often affected by the environment, and they needs often to deploy multiple devices. In addition, they can only get far and near information, and can not know the device's posture in space. In this paper, an UAV indoor positioning system is proposed based on monocular vision. Firstly, the image taken by the camera is used, and combined with the feature point method and the direct method, to track the feature points first, then the direct method is used to match the features according to the key points, and then the camera position and posture are estimated. Then, the depth filter is used to estimate the 3D depth of feature points, and a sparse map in the current environment is established. Finally, the real environment is simulated using the three-dimensional visualization tool RVIZ of Robot Operating System (ROS). The simulation results show that the proposed method can achieve good performance in indoor environment, and the positioning accuracy reaches 0.04 m.
At present, Unmanned Aerial Vehicle (UAV) positioning technology relies mainly on the represented Global Positioning System (GPS). However, it is difficult to locate where GPS signals are missing in the room. On the other hand, the traditional indoor positioning technology uses mainly Bluetooth, WiFi, base station positioning and other methods to merge into a set of positioning system. However, this kind of methods are often affected by the environment, and they needs often to deploy multiple devices. In addition, they can only get far and near information, and can not know the device's posture in space. In this paper, an UAV indoor positioning system is proposed based on monocular vision. Firstly, the image taken by the camera is used, and combined with the feature point method and the direct method, to track the feature points first, then the direct method is used to match the features according to the key points, and then the camera position and posture are estimated. Then, the depth filter is used to estimate the 3D depth of feature points, and a sparse map in the current environment is established. Finally, the real environment is simulated using the three-dimensional visualization tool RVIZ of Robot Operating System (ROS). The simulation results show that the proposed method can achieve good performance in indoor environment, and the positioning accuracy reaches 0.04 m.
2022, 44(3): 915-923.
doi: 10.11999/JEIT211177
Abstract:
A millimeter-wave air-terrestrial network with integrated access and backhaul is considered to investigate the impact of the Unmanned Aerial Vehicle (UAV) wireless backhaul on the network performance and user experience, where UAVs provide hotspot traffic services, Terrestrial Base Stations (TBSs) provide UAV backhaul links and serve users in non-hotspot areas, and a spectrum partitioning resource allocation method is considered for the access and backhaul links. For this scenario, a stochastic geometry-based framework is established to model the millimeter wave air-ground network, and derive the coverage probabilities of both users. Furthermore, based on the load analysis of TBSs and UAVs, the rate coverage performances are provided as well as the overall user performance. Based on the proposed analytical framework, the impacts of key system parameters, such as access link spectrum allocation ratio, UAV, and hotspot user densities, on user performance are studied.
A millimeter-wave air-terrestrial network with integrated access and backhaul is considered to investigate the impact of the Unmanned Aerial Vehicle (UAV) wireless backhaul on the network performance and user experience, where UAVs provide hotspot traffic services, Terrestrial Base Stations (TBSs) provide UAV backhaul links and serve users in non-hotspot areas, and a spectrum partitioning resource allocation method is considered for the access and backhaul links. For this scenario, a stochastic geometry-based framework is established to model the millimeter wave air-ground network, and derive the coverage probabilities of both users. Furthermore, based on the load analysis of TBSs and UAVs, the rate coverage performances are provided as well as the overall user performance. Based on the proposed analytical framework, the impacts of key system parameters, such as access link spectrum allocation ratio, UAV, and hotspot user densities, on user performance are studied.
2022, 44(3): 924-939.
doi: 10.11999/JEIT211410
Abstract:
In confrontational environment, capturing the communication topology of the Unmanned Aerial Vehicles (UAV) communication network helps us to discover efficiently and destroy its cluster function. However, under non-cooperative conditions, the traditional priori information of topology is difficult to obtain, and communication topology inference faces huge challenges. Existing related research is still in its infancy as a whole, the system model and inference mechanism are not clear, and the comparison of various methods in the same data dimension is also rare. Therefore, for the non-cooperative physical scene, firstly the system model is constructed and the inference mechanism is revealed. Then, the four methods of correlation, Granger causality, transfer entropy and multidimensional Hawkes process are simulated and compared. Finally, the prospects for the development of this research direction are prospected.
In confrontational environment, capturing the communication topology of the Unmanned Aerial Vehicles (UAV) communication network helps us to discover efficiently and destroy its cluster function. However, under non-cooperative conditions, the traditional priori information of topology is difficult to obtain, and communication topology inference faces huge challenges. Existing related research is still in its infancy as a whole, the system model and inference mechanism are not clear, and the comparison of various methods in the same data dimension is also rare. Therefore, for the non-cooperative physical scene, firstly the system model is constructed and the inference mechanism is revealed. Then, the four methods of correlation, Granger causality, transfer entropy and multidimensional Hawkes process are simulated and compared. Finally, the prospects for the development of this research direction are prospected.
2022, 44(3): 940-950.
doi: 10.11999/JEIT210662
Abstract:
As the future development direction of 6G, integrated space-air-ground communication well compensates for the drawback of insufficient current wireless communication coverage. In this paper, a Multi-Unmanned Aerial Vehicle (Multi-UAV) assisted communication algorithm with Multi-Agent Reinforcement Learning (MARL) is proposed to solve the Nash equilibrium approximate solution in a hybrid game model composed of users and UAVs and solve the joint optimization problem of UAV trajectory design, multidimensional resource scheduling and user access strategy in dynamic environment. The Markov game concept is exploited to model this continuous decision process with a Centralized Training Distributed Execution (CTDE) mechanism, and the Proximal Policy Optimization (PPO) algorithm is extended to the multi-agent domain. Two policy output modes are designed for the action space, where both the discrete and continuous actions coexist. Then, the implementation is improved by combining Beta policy. Finally, the effectiveness of the algorithm is verified by simulation experiments.
As the future development direction of 6G, integrated space-air-ground communication well compensates for the drawback of insufficient current wireless communication coverage. In this paper, a Multi-Unmanned Aerial Vehicle (Multi-UAV) assisted communication algorithm with Multi-Agent Reinforcement Learning (MARL) is proposed to solve the Nash equilibrium approximate solution in a hybrid game model composed of users and UAVs and solve the joint optimization problem of UAV trajectory design, multidimensional resource scheduling and user access strategy in dynamic environment. The Markov game concept is exploited to model this continuous decision process with a Centralized Training Distributed Execution (CTDE) mechanism, and the Proximal Policy Optimization (PPO) algorithm is extended to the multi-agent domain. Two policy output modes are designed for the action space, where both the discrete and continuous actions coexist. Then, the implementation is improved by combining Beta policy. Finally, the effectiveness of the algorithm is verified by simulation experiments.
2022, 44(3): 951-959.
doi: 10.11999/JEIT211312
Abstract:
A scenario of Unmanned Aerial Vehicle (UAV) assisted energy-constrained low-power Internet of Things (IoT) nodes for data transmission is considered, to address the problem of UAV coverage overlapped caused by the traditional Matérn Cluster Process (MCP) modeling, a Matérn Cluster under Distance Constraint (MCDC) strategy is proposed. The strategy uses the Matérn cluster process with distance constraints to model the locations of UAVs and IoT nodes, and achieves a significant reduction in redundant coverage. Under the MCDC strategy, the energy-constrained IoT nodes first harvest energy from the radio frequency signal sent by the UAV, and then use the harvested energy to transmit information to the UAV. The transmission probability of the IoT nodes, the outage performance, and the network throughput are analyzed; The time allocation ratio of the harvesting phase, the transmission power of the UAV, and the impact of the density of IoT nodes on the network performance are measured. Finally, the theoretical results are verified by simulation.
A scenario of Unmanned Aerial Vehicle (UAV) assisted energy-constrained low-power Internet of Things (IoT) nodes for data transmission is considered, to address the problem of UAV coverage overlapped caused by the traditional Matérn Cluster Process (MCP) modeling, a Matérn Cluster under Distance Constraint (MCDC) strategy is proposed. The strategy uses the Matérn cluster process with distance constraints to model the locations of UAVs and IoT nodes, and achieves a significant reduction in redundant coverage. Under the MCDC strategy, the energy-constrained IoT nodes first harvest energy from the radio frequency signal sent by the UAV, and then use the harvested energy to transmit information to the UAV. The transmission probability of the IoT nodes, the outage performance, and the network throughput are analyzed; The time allocation ratio of the harvesting phase, the transmission power of the UAV, and the impact of the density of IoT nodes on the network performance are measured. Finally, the theoretical results are verified by simulation.
2022, 44(3): 960-968.
doi: 10.11999/JEIT211368
Abstract:
Unmanned Aerial Vehicle (UAV) can be used as the air base station to cover flexibly hotspots by its mobility. However, it is challenging for the network operators to forecast the distribution of network traffic and optimize the deployment of UAVs. To solve this problem, an energy-efficient predictive deployment strategy of UAVs based on ConvLSTM with Attention mechanism (A-ConvLSTM) is proposed: a convolutional long short term memory deep spatio-temporal network model A-ConvLSTM with attention mechanism is proposed to forecast the spatio-temporal distribution of users and cellular traffic. Then based on the forecast, the coverage and locations of UAVs are optimized. On the premise of meeting the requirements of user access rate, an optimization formulation is established with the goal of minimizing the transmission power of UAVs. The formulation is decoupled into two subproblems and an energy-efficient deployment algorithm is proposed for iterative solution. The experimental results show that the performance of A-ConvLSTM is better than that of each baseline model. Energy-efficient deployment algorithm can effectively reduce the transmission power consumption of UAVs, and achieve the overall area coverage with fewer UAVs.
Unmanned Aerial Vehicle (UAV) can be used as the air base station to cover flexibly hotspots by its mobility. However, it is challenging for the network operators to forecast the distribution of network traffic and optimize the deployment of UAVs. To solve this problem, an energy-efficient predictive deployment strategy of UAVs based on ConvLSTM with Attention mechanism (A-ConvLSTM) is proposed: a convolutional long short term memory deep spatio-temporal network model A-ConvLSTM with attention mechanism is proposed to forecast the spatio-temporal distribution of users and cellular traffic. Then based on the forecast, the coverage and locations of UAVs are optimized. On the premise of meeting the requirements of user access rate, an optimization formulation is established with the goal of minimizing the transmission power of UAVs. The formulation is decoupled into two subproblems and an energy-efficient deployment algorithm is proposed for iterative solution. The experimental results show that the performance of A-ConvLSTM is better than that of each baseline model. Energy-efficient deployment algorithm can effectively reduce the transmission power consumption of UAVs, and achieve the overall area coverage with fewer UAVs.
2022, 44(3): 969-975.
doi: 10.11999/JEIT210429
Abstract:
In order to deal with the communication delay problem in an Unmanned Aerial Vehicle (UAV) enabled Base Station (BS) multicasting communication system, the online trajectory design for the UAV BS is investigated. A UAV BS is dispatched to disseminate common information to multiple ground users simultaneously in this system, where the locations of the ground users are random in each multicasting communication task. To ensure that the ground users can receive the complete multicasting information and considering the limited energy of the UAV, this paper focuses on minimizing the average duration for the UAV BS to complete the multicasting task. First, the considered problem is casted as a Markov Decision Process (MDP), and then the communication delay is introduced into the action value function. Finally, an online trajectory optimization algorithm based on the Q-Learning algorithm is proposed to minimize the average duration for the UAV BS to complete the multicasting task. Simulation results show that the proposed algorithm can effectively optimize the trajectory of the UAV BS for its multicasting task in an online manner and can effectively reduce the duration of the multicast task, as compared to other benchmark schemes.
In order to deal with the communication delay problem in an Unmanned Aerial Vehicle (UAV) enabled Base Station (BS) multicasting communication system, the online trajectory design for the UAV BS is investigated. A UAV BS is dispatched to disseminate common information to multiple ground users simultaneously in this system, where the locations of the ground users are random in each multicasting communication task. To ensure that the ground users can receive the complete multicasting information and considering the limited energy of the UAV, this paper focuses on minimizing the average duration for the UAV BS to complete the multicasting task. First, the considered problem is casted as a Markov Decision Process (MDP), and then the communication delay is introduced into the action value function. Finally, an online trajectory optimization algorithm based on the Q-Learning algorithm is proposed to minimize the average duration for the UAV BS to complete the multicasting task. Simulation results show that the proposed algorithm can effectively optimize the trajectory of the UAV BS for its multicasting task in an online manner and can effectively reduce the duration of the multicast task, as compared to other benchmark schemes.
2022, 44(3): 976-986.
doi: 10.11999/JEIT210491
Abstract:
In order to utilize better the surrounding radio-frequency energy and improve the operation lifetime of Device-to-Device (D2D) communications as well as the spectrum efficiency of Unmanned Aerial Vehicle (UAV) communication, a resource allocation algorithm is proposed for UAV-D2D networks with energy harvesting. Considering the constraints of the maximum transmit power and the mobility of the UAV, the minimum rate requirements of both cellular users and D2D users, a multivariable coupling resource allocation problem is formulated to maximize the sum rates of both cellular users and D2D users. The mixed-integer nonlinear programming problem is transformed into a convex optimization problem by using the successive convex approximation and variable substitution methods, where the closed-form solutions are obtained by the using Lagrange dual method. Simulation results demonstrate that the proposed algorithm has good convergence performance and higher system capacity.
In order to utilize better the surrounding radio-frequency energy and improve the operation lifetime of Device-to-Device (D2D) communications as well as the spectrum efficiency of Unmanned Aerial Vehicle (UAV) communication, a resource allocation algorithm is proposed for UAV-D2D networks with energy harvesting. Considering the constraints of the maximum transmit power and the mobility of the UAV, the minimum rate requirements of both cellular users and D2D users, a multivariable coupling resource allocation problem is formulated to maximize the sum rates of both cellular users and D2D users. The mixed-integer nonlinear programming problem is transformed into a convex optimization problem by using the successive convex approximation and variable substitution methods, where the closed-form solutions are obtained by the using Lagrange dual method. Simulation results demonstrate that the proposed algorithm has good convergence performance and higher system capacity.
2022, 44(3): 987-995.
doi: 10.11999/JEIT211020
Abstract:
The Unmanned Aerial Vehicle (UAV) can provide convenient and effective supplementary communication solutions for 6G networks since it gets the advantages of flexibility, adaptability and high mobility. To improve further the spectrum efficiency and reduce the transmission time required for full data exchange, a Full-Duplex (FD) Multi-Way Relay Network Non-Orthogonal Multiple Access (NOMA) transmission scheme (FD NOMA MWRN) is proposed. In addition, a more practical case is assumed that in-phase/quadrature imbalance of transceiver is taken into account. Based on this, the transmission rate and energy efficiency are analyzed. The simulation results indicate the following conclusions. Firstly, the full-duplex transmission can improve the spectral utilization compared to the Half-Duplex (HD) mode. Secondly, the proposed scheme, which consumes the number of time slots is always one regardless of the number of users, has better performance than the Orthogonal Multiple Access (OMA) scheme. Thirdly, the In-phase/Quadrature (I/Q) imbalance and the working height of the UAV both limit the transmission rate of the system.
The Unmanned Aerial Vehicle (UAV) can provide convenient and effective supplementary communication solutions for 6G networks since it gets the advantages of flexibility, adaptability and high mobility. To improve further the spectrum efficiency and reduce the transmission time required for full data exchange, a Full-Duplex (FD) Multi-Way Relay Network Non-Orthogonal Multiple Access (NOMA) transmission scheme (FD NOMA MWRN) is proposed. In addition, a more practical case is assumed that in-phase/quadrature imbalance of transceiver is taken into account. Based on this, the transmission rate and energy efficiency are analyzed. The simulation results indicate the following conclusions. Firstly, the full-duplex transmission can improve the spectral utilization compared to the Half-Duplex (HD) mode. Secondly, the proposed scheme, which consumes the number of time slots is always one regardless of the number of users, has better performance than the Orthogonal Multiple Access (OMA) scheme. Thirdly, the In-phase/Quadrature (I/Q) imbalance and the working height of the UAV both limit the transmission rate of the system.
2022, 44(3): 996-1005.
doi: 10.11999/JEIT211205
Abstract:
Frequent disasters and accidents in underground space pose severe challenges to the rapid reconfiguration of emergency communication networks and the real-time transmission of disaster information in extreme environments. It is urgent to build the Unmanned Aerial Vehicle (UAV) emergency communication networks with the capabilities of dynamic reconstruction and real-time response. For the problems of frequent failure of network connectivity caused by dynamic uncertainties such as rapidly changing topologies, after extracting and simplifying the key topology information using graph theory, the Coalitional Game (CG) is combined with time-varying topology graphs and the Coalitional Graph Game based Adaptive Topology Control (CGG-ATC) algorithm, which can maintain the connectivity through collaborative establishment of Long-range Links (LLs), is proposed. The simulation results shows that the proposed algorithm can achieve the better trade-off among connectivity, average transmission delay, and link cost compared with other existing algorithms. Besides, due to its fast convergence speed, the network decision is elastic and adaptive with the rapid topology changes when considering the dynamic uncertainties of post-disaster scenarios.
Frequent disasters and accidents in underground space pose severe challenges to the rapid reconfiguration of emergency communication networks and the real-time transmission of disaster information in extreme environments. It is urgent to build the Unmanned Aerial Vehicle (UAV) emergency communication networks with the capabilities of dynamic reconstruction and real-time response. For the problems of frequent failure of network connectivity caused by dynamic uncertainties such as rapidly changing topologies, after extracting and simplifying the key topology information using graph theory, the Coalitional Game (CG) is combined with time-varying topology graphs and the Coalitional Graph Game based Adaptive Topology Control (CGG-ATC) algorithm, which can maintain the connectivity through collaborative establishment of Long-range Links (LLs), is proposed. The simulation results shows that the proposed algorithm can achieve the better trade-off among connectivity, average transmission delay, and link cost compared with other existing algorithms. Besides, due to its fast convergence speed, the network decision is elastic and adaptive with the rapid topology changes when considering the dynamic uncertainties of post-disaster scenarios.
2022, 44(3): 1006-1013.
doi: 10.11999/JEIT211372
Abstract:
To avoid the high detection in long-distance covert communications due to the large transmission power, an Unmanned Aerial Vehicle (UAV)-relay based covert communication scheme with finite block block-length is proposed in this paper. Firstly, the Signal-to -Noise-Ratio (SNR) at the legal receiver via the UAV-relay is derived, based on which the outage probability and throughput of the covert communication link are also obtained. Secondly, the detection performance of the monitor is analyzed, i.e. KL (Kullback-Leibler) scatter, and it is used as a constraint on the covert communication. Finally, to maximize the throughput of the covert communication, the transmission power at the transmitter and the UAV-relay, as well as the flight height of UAV are jointly optimized. The simulation results verify the performance of the proposed covert communication scheme, and also show its superiority to the traditional strategies without UAVs since it greatly reduces the KL divergence at the warder.
To avoid the high detection in long-distance covert communications due to the large transmission power, an Unmanned Aerial Vehicle (UAV)-relay based covert communication scheme with finite block block-length is proposed in this paper. Firstly, the Signal-to -Noise-Ratio (SNR) at the legal receiver via the UAV-relay is derived, based on which the outage probability and throughput of the covert communication link are also obtained. Secondly, the detection performance of the monitor is analyzed, i.e. KL (Kullback-Leibler) scatter, and it is used as a constraint on the covert communication. Finally, to maximize the throughput of the covert communication, the transmission power at the transmitter and the UAV-relay, as well as the flight height of UAV are jointly optimized. The simulation results verify the performance of the proposed covert communication scheme, and also show its superiority to the traditional strategies without UAVs since it greatly reduces the KL divergence at the warder.
2022, 44(3): 1014-1023.
doi: 10.11999/JEIT210063
Abstract:
With the increasing complexity of space communication tasks, especially the increasing demand for time sensitivity, on the one hand, the performances of high bandwidth, high reliability and real time in intra satellite systems are required; on the other hand, low latency and high reliability are demanded for inter satellite wireless links. However, due to the large difference between the satellite internal wired link and the inter-satellite wireless link, when the data is transmitted from the wired link to wireless link, it is easy to cause congestion problem on node, and can not guarantee the bounded latency for time-sensitive service. To improve the real time performance for data transmission in space information network, a flow scheduling scheme in wired and wireless converged Time-Sensitive Network (TSN) is proposed. Firstly, the relationship between terminal delay requirement and wired/wireless link resources is analyzed. Then, the time-sensitive requirements of terminals are collected by TSN controller and the optimization goal is determined by the end-to-end minimum average delay of time-sensitive flow in the entire network. Moreover, the enhanced elite retention genetic algorithm is adopted to solve quickly the scheme. Finally, the performance of proposed time slot allocation algorithm is verified through Pycharm. Meanwhile, a low-orbit satellite network scenario is implemented under EXata network simulation platform to evaluate the proposed scheme in the further. The results demonstrate that the proposed wired and wireless joint stream scheduling scheme can provide bounded and stable delay for space time-sensitive tasks.
With the increasing complexity of space communication tasks, especially the increasing demand for time sensitivity, on the one hand, the performances of high bandwidth, high reliability and real time in intra satellite systems are required; on the other hand, low latency and high reliability are demanded for inter satellite wireless links. However, due to the large difference between the satellite internal wired link and the inter-satellite wireless link, when the data is transmitted from the wired link to wireless link, it is easy to cause congestion problem on node, and can not guarantee the bounded latency for time-sensitive service. To improve the real time performance for data transmission in space information network, a flow scheduling scheme in wired and wireless converged Time-Sensitive Network (TSN) is proposed. Firstly, the relationship between terminal delay requirement and wired/wireless link resources is analyzed. Then, the time-sensitive requirements of terminals are collected by TSN controller and the optimization goal is determined by the end-to-end minimum average delay of time-sensitive flow in the entire network. Moreover, the enhanced elite retention genetic algorithm is adopted to solve quickly the scheme. Finally, the performance of proposed time slot allocation algorithm is verified through Pycharm. Meanwhile, a low-orbit satellite network scenario is implemented under EXata network simulation platform to evaluate the proposed scheme in the further. The results demonstrate that the proposed wired and wireless joint stream scheduling scheme can provide bounded and stable delay for space time-sensitive tasks.
2022, 44(3): 1024-1033.
doi: 10.11999/JEIT210145
Abstract:
For a point-to-point energy harvesting wireless communication system equipped with energy harvesting devices at the source node, to maximize the long-term average transmission rate, an online power control and adaptive modulation joint optimization strategy based on Lyapunov optimization framework is proposed. Due to the randomness of the energy arrival and the channel state, the optimization problem is a stochastic optimization problem. By using Lyapunov optimization framework, the long-term optimization problem under the constraints of battery operation and available energy is transformed into a joint optimization problem of the transmission power, the modulation mode and the frame length to minimize the virtual queue drift-plus-penalty" per time slot. The proposed algorithm only dependes on the current channel state and the battery state. The simulation results show that the proposed algorithm can effectively utilize the harvested energy and adapt to the channel changes. The long-term average actual achievable information transmission rate is significantly better than the greedy and the half-power algorithm. Compared with the offline water filling algorithm and other comparison algorithms, both which aim at maximizing the channel capacity, the proposed algorithm also can achieve a higher actual transmission rate.
For a point-to-point energy harvesting wireless communication system equipped with energy harvesting devices at the source node, to maximize the long-term average transmission rate, an online power control and adaptive modulation joint optimization strategy based on Lyapunov optimization framework is proposed. Due to the randomness of the energy arrival and the channel state, the optimization problem is a stochastic optimization problem. By using Lyapunov optimization framework, the long-term optimization problem under the constraints of battery operation and available energy is transformed into a joint optimization problem of the transmission power, the modulation mode and the frame length to minimize the virtual queue drift-plus-penalty" per time slot. The proposed algorithm only dependes on the current channel state and the battery state. The simulation results show that the proposed algorithm can effectively utilize the harvested energy and adapt to the channel changes. The long-term average actual achievable information transmission rate is significantly better than the greedy and the half-power algorithm. Compared with the offline water filling algorithm and other comparison algorithms, both which aim at maximizing the channel capacity, the proposed algorithm also can achieve a higher actual transmission rate.
2022, 44(3): 1034-1043.
doi: 10.11999/JEIT210056
Abstract:
To solve the sensitivity of sparse stepped-frequency chirp signals to target radial motion and to achieve high-resolution imaging with low Signal to Noise Ratio (SNR), a translation compensation and high-resolution Inverse Synthetic Aperture Radar (ISAR) imaging based on genetic algorithm and sparse Bayesian learning is proposed. Firstly, an echo model and a sparse observation model are established for the sparse stepped-frequency chirp signal. A parameterized dictionary is then constructed to turn ISAR imaging to the joint estimation of target motion parameter and High-Resolution Range Profile (HRRP) synthesis. Secondly, the Gamma-Gaussian prior is introduced to the high-resolution range profile of the target, and the scattering center is estimated by the Variational Bayesian Inference (VBI) algorithm. On this basis, target motion parameters and high-quality HRRP are obtained through the iteration of genetic algorithm. Hence, high-resolution imaging of the moving targets is achieved while the motion parameters are accurately estimated. The effectiveness of the proposed method is verified by simulation and real data processing result in various scenes.
To solve the sensitivity of sparse stepped-frequency chirp signals to target radial motion and to achieve high-resolution imaging with low Signal to Noise Ratio (SNR), a translation compensation and high-resolution Inverse Synthetic Aperture Radar (ISAR) imaging based on genetic algorithm and sparse Bayesian learning is proposed. Firstly, an echo model and a sparse observation model are established for the sparse stepped-frequency chirp signal. A parameterized dictionary is then constructed to turn ISAR imaging to the joint estimation of target motion parameter and High-Resolution Range Profile (HRRP) synthesis. Secondly, the Gamma-Gaussian prior is introduced to the high-resolution range profile of the target, and the scattering center is estimated by the Variational Bayesian Inference (VBI) algorithm. On this basis, target motion parameters and high-quality HRRP are obtained through the iteration of genetic algorithm. Hence, high-resolution imaging of the moving targets is achieved while the motion parameters are accurately estimated. The effectiveness of the proposed method is verified by simulation and real data processing result in various scenes.
2022, 44(3): 1044-1051.
doi: 10.11999/JEIT210135
Abstract:
The wide-swath interferometric imaging radar altimeter with short baseline and at near nadir angles will satisfy the requirement of high precision, high temporal and spatial resolution Sea Surface Height (SSH) for sub-mesoscale ocean features. In the process of retrieving sea level, interferometric phase filtering is an important part of suppressing random phase noise and maintaining the details of phase edges. The varying random noise of flattened phase will be attenuated effectively with total variation regularization filtering which based on the features of noise distribution along the cross-track direction for the altimeter. Simulation results show the STandard Deviation (STD) of the filtered phase error using the proposed method is reduced from 0.32 rad to 0.023 rad, and the maximum deviation is less than 0.001 rad within the swath. The distribution of the phase error accuracy using the proposed method is more homogeneous compared to traditional phase filtering methods. Simulation results also show the proposed method can preserve the resolution and edge information, which provides an effective guarantee for the consistency of sea surface elevation accuracy.
The wide-swath interferometric imaging radar altimeter with short baseline and at near nadir angles will satisfy the requirement of high precision, high temporal and spatial resolution Sea Surface Height (SSH) for sub-mesoscale ocean features. In the process of retrieving sea level, interferometric phase filtering is an important part of suppressing random phase noise and maintaining the details of phase edges. The varying random noise of flattened phase will be attenuated effectively with total variation regularization filtering which based on the features of noise distribution along the cross-track direction for the altimeter. Simulation results show the STandard Deviation (STD) of the filtered phase error using the proposed method is reduced from 0.32 rad to 0.023 rad, and the maximum deviation is less than 0.001 rad within the swath. The distribution of the phase error accuracy using the proposed method is more homogeneous compared to traditional phase filtering methods. Simulation results also show the proposed method can preserve the resolution and edge information, which provides an effective guarantee for the consistency of sea surface elevation accuracy.
2022, 44(3): 1052-1058.
doi: 10.11999/JEIT210038
Abstract:
Due to the good estimation performance in the case of off-grid, the gridless DOA estimation algorithms attract extensive attentions and researches in recent years, among which the most representative is the one based on Atomic Norm Minimization (ANM). With the development of Decoupled ANM (DANM) algorithm, the application of ANM to the field of two-dimensional DOA estimation is possible. However, the traditional DANM algorithm and its subsequent improved algorithms are only suitable for Uniform Rectangular Array (URA) or Sparse Rectangular Array (SRA), and is not suitable for planar arrays with arbitrary geometry. In order to solve the above problem, a gridless DOA estimation algorithm, B-DANM algorithm, is proposed for planar arrays with arbitrary geometry. B-DANM algorithm exploits the first Bessel function to expand the covariance data of the received signal of the actual planar antenna array, so as to obtain the DANM algorithm framework suitable for planar arrays with arbitrary geometry, and then the final DOA estimation result is obtained by solving the Semi-Definite Program (SDP) problem, Vandermonde decomposition of Toeplitz matrix, pairing of estimation parameters and angle transformation. The simulation results show that the B-DANM algorithm has the advantages of accuracy and resolution compared with the traditional two-dimensional DOA estimation algorithm in the direction finding system of planar arrays with arbitrary geometry.
Due to the good estimation performance in the case of off-grid, the gridless DOA estimation algorithms attract extensive attentions and researches in recent years, among which the most representative is the one based on Atomic Norm Minimization (ANM). With the development of Decoupled ANM (DANM) algorithm, the application of ANM to the field of two-dimensional DOA estimation is possible. However, the traditional DANM algorithm and its subsequent improved algorithms are only suitable for Uniform Rectangular Array (URA) or Sparse Rectangular Array (SRA), and is not suitable for planar arrays with arbitrary geometry. In order to solve the above problem, a gridless DOA estimation algorithm, B-DANM algorithm, is proposed for planar arrays with arbitrary geometry. B-DANM algorithm exploits the first Bessel function to expand the covariance data of the received signal of the actual planar antenna array, so as to obtain the DANM algorithm framework suitable for planar arrays with arbitrary geometry, and then the final DOA estimation result is obtained by solving the Semi-Definite Program (SDP) problem, Vandermonde decomposition of Toeplitz matrix, pairing of estimation parameters and angle transformation. The simulation results show that the B-DANM algorithm has the advantages of accuracy and resolution compared with the traditional two-dimensional DOA estimation algorithm in the direction finding system of planar arrays with arbitrary geometry.
2022, 44(3): 1059-1066.
doi: 10.11999/JEIT210113
Abstract:
The complex spatial variant Doppler along the range dimension has large influence on SAR imaging quality, the normal range Doppler (RD) algorithm is easy to produce the phenomenon of alternating light and dark, image visibility is poor. During azimuth pre-filtering and azimuth pulse compression estimation or calculation the Doppler frequency along range block/gate, in azimuth pre-filtering and azimuth pulse compression, the window function is moved along the azimuth according to Doppler frequency can effectively solve the phenomenon of chiaroscuro, however, this method can not improve the focusing effect in near or far range. Based on the above problems, an improved motion compensation method is proposed in this paper, motion compensation and motion correction are performed by dividing the blocks along the range dimension, collimation of a spatially variable Doppler along range and then azimuth pulse compression is carried out. There is no alternation of light and dark in the proposed method processing results, the image is continuous and complete along the range dimension and has better visibility. At the same time, the image has a good focusing effect at far distance from the range center. The validity of the algorithm is verified by the measured data.
The complex spatial variant Doppler along the range dimension has large influence on SAR imaging quality, the normal range Doppler (RD) algorithm is easy to produce the phenomenon of alternating light and dark, image visibility is poor. During azimuth pre-filtering and azimuth pulse compression estimation or calculation the Doppler frequency along range block/gate, in azimuth pre-filtering and azimuth pulse compression, the window function is moved along the azimuth according to Doppler frequency can effectively solve the phenomenon of chiaroscuro, however, this method can not improve the focusing effect in near or far range. Based on the above problems, an improved motion compensation method is proposed in this paper, motion compensation and motion correction are performed by dividing the blocks along the range dimension, collimation of a spatially variable Doppler along range and then azimuth pulse compression is carried out. There is no alternation of light and dark in the proposed method processing results, the image is continuous and complete along the range dimension and has better visibility. At the same time, the image has a good focusing effect at far distance from the range center. The validity of the algorithm is verified by the measured data.
2022, 44(3): 1067-1074.
doi: 10.11999/JEIT210035
Abstract:
The microwave photonic channelizer can convert the Radio Frequency (RF) signal into the optical domain for transmission and procession, avoiding effectively the limitation of electronic bottleneck, and realizing the instantaneous reception of ultra-wideband signal or multi-frequency signals, which can be perfectly applied to radar system and electronic warfare. In this paper, a microwave photonic channelizer based on dual-output image-reject mixer is proposed, both the signal path and the local oscillator path are divided into three paths by using an optical coupler. An acousto-optic frequency shifter is used by the optical local oscillator to shift the frequency to the left and right and then enters the image rejection mixer with the signal path. Finally, a 6 GHz bandwidth RF signal is divided into 6 subchannels with a bandwidth of 1 GHz to achieve simultaneous reception. This scheme needs no optical frequency comb and doubles the channelization efficiency, the image rejection ratio of the sub-channels all exceeds 22 dB, and the spurious-free dynamic range of the system can reach 91.4 dB·Hz 2∕3.
The microwave photonic channelizer can convert the Radio Frequency (RF) signal into the optical domain for transmission and procession, avoiding effectively the limitation of electronic bottleneck, and realizing the instantaneous reception of ultra-wideband signal or multi-frequency signals, which can be perfectly applied to radar system and electronic warfare. In this paper, a microwave photonic channelizer based on dual-output image-reject mixer is proposed, both the signal path and the local oscillator path are divided into three paths by using an optical coupler. An acousto-optic frequency shifter is used by the optical local oscillator to shift the frequency to the left and right and then enters the image rejection mixer with the signal path. Finally, a 6 GHz bandwidth RF signal is divided into 6 subchannels with a bandwidth of 1 GHz to achieve simultaneous reception. This scheme needs no optical frequency comb and doubles the channelization efficiency, the image rejection ratio of the sub-channels all exceeds 22 dB, and the spurious-free dynamic range of the system can reach 91.4 dB·Hz 2∕3.
2022, 44(3): 1075-1085.
doi: 10.11999/JEIT210049
Abstract:
In recent years, the form of invoice has changed from the traditional paper invoice to e-invoice. Compared with issuing paper invoice, there are the advantages of simpler process, lower cost, and easier storage for online issuing e-invoice. However, how to ensure the legitimacy of user identity and the privacy of identity information in online issuing e-invoice service is the focus issue of current research. In order to solve this issue, by using the pre- shared key mechanism, a privacy preserving authentication scheme for online issuing e-invoice is proposed. By this scheme, a legitimate user who has completed a transaction with an enterprise can initiate an e-invoice request locally and online, and the e-invoice system of the State Administration of taxation can provide the user with e-invoices after verifying the identity information and transaction information successfully. Security and performance analysis results show that the proposed scheme can provide robust security properties with less authentication overhead.
In recent years, the form of invoice has changed from the traditional paper invoice to e-invoice. Compared with issuing paper invoice, there are the advantages of simpler process, lower cost, and easier storage for online issuing e-invoice. However, how to ensure the legitimacy of user identity and the privacy of identity information in online issuing e-invoice service is the focus issue of current research. In order to solve this issue, by using the pre- shared key mechanism, a privacy preserving authentication scheme for online issuing e-invoice is proposed. By this scheme, a legitimate user who has completed a transaction with an enterprise can initiate an e-invoice request locally and online, and the e-invoice system of the State Administration of taxation can provide the user with e-invoices after verifying the identity information and transaction information successfully. Security and performance analysis results show that the proposed scheme can provide robust security properties with less authentication overhead.
2022, 44(3): 1086-1092.
doi: 10.11999/JEIT210155
Abstract:
The development of cloud storage technology achieves resource sharing, which reduces users data management overhead. Searchable encryption technology protects users privacy and supports ciphertext retrieval, making it easy for users to find encrypted data in the cloud. Although existing public key searchable encryption schemes support authentication, the denial property is not implemented. To protect better the senders identity privacy, an Identity-based Public Key keyword Searchable Encryption scheme with Denial Authentication (IDAPKSE) is proposed. In the proposed scheme, the sender uploads the ciphertext and has the ability to deny that he or she uploaded the ciphertext to the cloud server. At the same time, the receiver can confirm the origin of the ciphertext, however, even with the cooperation of a third party, the receiver can not prove the facts in his/her possession to the third party. Under the random oracle model, based on the Bilinear Diffie-Hellman(BDH) and Decisional Bilinear Diffie-Hellman(DBDH) assumptions, the proposed scheme satisfies unforgeability of the ciphertexts, and indistinguishability of ciphertexts and trapdoors.
The development of cloud storage technology achieves resource sharing, which reduces users data management overhead. Searchable encryption technology protects users privacy and supports ciphertext retrieval, making it easy for users to find encrypted data in the cloud. Although existing public key searchable encryption schemes support authentication, the denial property is not implemented. To protect better the senders identity privacy, an Identity-based Public Key keyword Searchable Encryption scheme with Denial Authentication (IDAPKSE) is proposed. In the proposed scheme, the sender uploads the ciphertext and has the ability to deny that he or she uploaded the ciphertext to the cloud server. At the same time, the receiver can confirm the origin of the ciphertext, however, even with the cooperation of a third party, the receiver can not prove the facts in his/her possession to the third party. Under the random oracle model, based on the Bilinear Diffie-Hellman(BDH) and Decisional Bilinear Diffie-Hellman(DBDH) assumptions, the proposed scheme satisfies unforgeability of the ciphertexts, and indistinguishability of ciphertexts and trapdoors.
2022, 44(3): 1093-1101.
doi: 10.11999/JEIT210133
Abstract:
Salient object detection occupies an important position in the field of computer vision. How to deal with feature information on different scales becomes the key to obtain excellent prediction results. Two contributions are made in this article. On the one hand, a feature permutation method for salient object detection is proposed. The proposed method is a convolutional neural network based on the self-encoding network structure. It uses the concept of scale representation proposed in this paper to group and permute the multiscale feature maps of different layers in the neural network. So the proposed method obtains a more generalized salient object detection model and a more accurate prediction results about salient object detection. On the other hand, the proposed method adopts the double-conv residual and FReLU activation for the output of the model, so that more complete pixel information could be obtained, and the spatial information is also activated as well. The characteristics of the two algorithms are fused to act on the learning and training of the model. Finally, the proposed algorithm is compared with the mainstream salient object detection algorithms, and the experimental results show that the proposed algorithm obtains the best results from all.
Salient object detection occupies an important position in the field of computer vision. How to deal with feature information on different scales becomes the key to obtain excellent prediction results. Two contributions are made in this article. On the one hand, a feature permutation method for salient object detection is proposed. The proposed method is a convolutional neural network based on the self-encoding network structure. It uses the concept of scale representation proposed in this paper to group and permute the multiscale feature maps of different layers in the neural network. So the proposed method obtains a more generalized salient object detection model and a more accurate prediction results about salient object detection. On the other hand, the proposed method adopts the double-conv residual and FReLU activation for the output of the model, so that more complete pixel information could be obtained, and the spatial information is also activated as well. The characteristics of the two algorithms are fused to act on the learning and training of the model. Finally, the proposed algorithm is compared with the mainstream salient object detection algorithms, and the experimental results show that the proposed algorithm obtains the best results from all.
2022, 44(3): 1102-1110.
doi: 10.11999/JEIT210131
Abstract:
Convolutional Recurrent Neural Network (CRNN), which cascades Convolutional Neural Network (CNN) structure and Recurrent Neural Network (RNN) structure, and its reformations are the mainstreams for sound event detection. However, CRNN models trained in end-to-end way can not make CNN and RNN structures have meaningful functions, which may affect the performances of sound event detection. To alleviate this problem, this paper proposes an Audio Tagging Consistency Constraint CRNN (ATCC-CRNN) method for sound event detection. In ATCC-CRNN, a CRNN audio tagging branch is embedded in the sound event classification network, meanwhile a CNN audio tagging network is designed to predict the audio tag of CNN structure. Thereafter, in the training stage of CRNN, the audio tagging prediction results of CNN and CRNN are limited to be consistent to make the CNN structure concentrating on audio tagging task and the RNN structure concentrating on modelling the inter-frame relationship of audio sample. As a result, the CNN structure and RNN structure of CRNN have different feature description functions for sound event detection. Experiments are carried out on the dataset of IEEE DCASE 2019 domestic environments sound event detection task (task 4). Experimental results demonstrate that the proposed ATCC-CRNN method improves significantly the performance of CRNN model in sound event detection. The event-based F1 scores on validation dataset and evaluation dataset are improved by more than 3.7%. These results indicate that the proposed ATCC-CRNN makes the CNN and RNN structures of CRNN functional clearly and improves the generalization ability of CRNN sound event detection model.
Convolutional Recurrent Neural Network (CRNN), which cascades Convolutional Neural Network (CNN) structure and Recurrent Neural Network (RNN) structure, and its reformations are the mainstreams for sound event detection. However, CRNN models trained in end-to-end way can not make CNN and RNN structures have meaningful functions, which may affect the performances of sound event detection. To alleviate this problem, this paper proposes an Audio Tagging Consistency Constraint CRNN (ATCC-CRNN) method for sound event detection. In ATCC-CRNN, a CRNN audio tagging branch is embedded in the sound event classification network, meanwhile a CNN audio tagging network is designed to predict the audio tag of CNN structure. Thereafter, in the training stage of CRNN, the audio tagging prediction results of CNN and CRNN are limited to be consistent to make the CNN structure concentrating on audio tagging task and the RNN structure concentrating on modelling the inter-frame relationship of audio sample. As a result, the CNN structure and RNN structure of CRNN have different feature description functions for sound event detection. Experiments are carried out on the dataset of IEEE DCASE 2019 domestic environments sound event detection task (task 4). Experimental results demonstrate that the proposed ATCC-CRNN method improves significantly the performance of CRNN model in sound event detection. The event-based F1 scores on validation dataset and evaluation dataset are improved by more than 3.7%. These results indicate that the proposed ATCC-CRNN makes the CNN and RNN structures of CRNN functional clearly and improves the generalization ability of CRNN sound event detection model.
2022, 44(3): 1111-1118.
doi: 10.11999/JEIT210459
Abstract:
Aspect level sentiment analysis aims to identify the sentiment polarity of a specific aspect in a given context, and is a fine-grained sentiment analysis task. The traditional attention-based approach, which only performs the semantic interaction between words, does not establish the syntactic relation interaction between aspect words and text words, resulting in the aspect words incorrectly focusing on information about words that are irrelevant to their syntax. In addition, the positional distance feature and the syntactic distance feature of words, which reflect their relationships in the linear form of the sentence and in the syntactic dependency tree of the sentence, respectively, are ignored by the method of processing syntactic information using graph convolutional networks, allowing irrelevant information far from the aspect words to interfere with their sentiment analysis. To address this problem, a Multi-Interaction Graph Convolutional Network (MIGCN) is proposed. First, the context words positional distance features are fed into each layer of the graph convolutional network, while the adjacency matrix of the graph convolutional network is weighted by using the syntactic distance of context words in the dependency tree. Finally, semantic interaction and syntactic interaction are designed to process the semantic and syntactic information between words, respectively. The experimental results show the proposed model can outperform state-of-the-art baselines on the available datasets.
Aspect level sentiment analysis aims to identify the sentiment polarity of a specific aspect in a given context, and is a fine-grained sentiment analysis task. The traditional attention-based approach, which only performs the semantic interaction between words, does not establish the syntactic relation interaction between aspect words and text words, resulting in the aspect words incorrectly focusing on information about words that are irrelevant to their syntax. In addition, the positional distance feature and the syntactic distance feature of words, which reflect their relationships in the linear form of the sentence and in the syntactic dependency tree of the sentence, respectively, are ignored by the method of processing syntactic information using graph convolutional networks, allowing irrelevant information far from the aspect words to interfere with their sentiment analysis. To address this problem, a Multi-Interaction Graph Convolutional Network (MIGCN) is proposed. First, the context words positional distance features are fed into each layer of the graph convolutional network, while the adjacency matrix of the graph convolutional network is weighted by using the syntactic distance of context words in the dependency tree. Finally, semantic interaction and syntactic interaction are designed to process the semantic and syntactic information between words, respectively. The experimental results show the proposed model can outperform state-of-the-art baselines on the available datasets.
2022, 44(3): 1119-1128.
doi: 10.11999/JEIT210119
Abstract:
In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user's perceived quality or cost value, how to establish a suitable user recruitment mechanism is the key issue that this article needs to solve. It is necessary to try to ensure the efficiency and profit maximization of the mobile crowdsensing platform. Therefore, a mobile crowdsensing user recruitment algorithm based on a Combined Multi-Armed Bandit (CMAB) is proposed to solve the recruitment problem of known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multi-arm bandit model. Each rocker is represented by a different user’s choice, and the income obtained represents the user’s perceived quality. Secondly, the Upper Confidence Bound (UCB) algorithm is proposed to update the user’s perceived quality according to the completion of the task. Users’ perceived quality values are sorted from high to low, and then the user with the largest ratio of perceived quality to recruitment cost is selected under budget conditions, tasks are assigned, and their perceived quality is updated. Finally, A large number of experimental simulations based on real data sets are carried out to verify the feasibility and effectiveness of the algorithm.
In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user's perceived quality or cost value, how to establish a suitable user recruitment mechanism is the key issue that this article needs to solve. It is necessary to try to ensure the efficiency and profit maximization of the mobile crowdsensing platform. Therefore, a mobile crowdsensing user recruitment algorithm based on a Combined Multi-Armed Bandit (CMAB) is proposed to solve the recruitment problem of known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multi-arm bandit model. Each rocker is represented by a different user’s choice, and the income obtained represents the user’s perceived quality. Secondly, the Upper Confidence Bound (UCB) algorithm is proposed to update the user’s perceived quality according to the completion of the task. Users’ perceived quality values are sorted from high to low, and then the user with the largest ratio of perceived quality to recruitment cost is selected under budget conditions, tasks are assigned, and their perceived quality is updated. Finally, A large number of experimental simulations based on real data sets are carried out to verify the feasibility and effectiveness of the algorithm.
2022, 44(3): 1129-1136.
doi: 10.11999/JEIT210163
Abstract:
Dense crowd counting is a classic problem in the field of computer vision, and it is still subject to the influence of factors such as uneven scale, noise and occlusion. This paper proposes a dense crowd counting method based on a new multi-scale attention mechanism. Deep network includes backbone network, feature extraction network and feature fusion network. Among them, the feature extraction network includes feature branch and attention branch. It adopts a new multi-scale module composed of parallel convolution kernel functions, which can better obtain the characteristics of people at different scales to adapt to the uneven scale of dense population distribution features; The feature fusion network uses the attention fusion module to enhance the output features of the feature extraction network, realizes the effective fusion of attention features and image features, and improves counting accuracy. Experiments on public data sets such as ShanghaiTech, UCF_CC_50, Mall and UCSD show that the proposed method outperforms existing methods in both MAE and MSE indicators.
Dense crowd counting is a classic problem in the field of computer vision, and it is still subject to the influence of factors such as uneven scale, noise and occlusion. This paper proposes a dense crowd counting method based on a new multi-scale attention mechanism. Deep network includes backbone network, feature extraction network and feature fusion network. Among them, the feature extraction network includes feature branch and attention branch. It adopts a new multi-scale module composed of parallel convolution kernel functions, which can better obtain the characteristics of people at different scales to adapt to the uneven scale of dense population distribution features; The feature fusion network uses the attention fusion module to enhance the output features of the feature extraction network, realizes the effective fusion of attention features and image features, and improves counting accuracy. Experiments on public data sets such as ShanghaiTech, UCF_CC_50, Mall and UCSD show that the proposed method outperforms existing methods in both MAE and MSE indicators.
2022, 44(3): 1137-1146.
doi: 10.11999/JEIT210023
Abstract:
To improve the security of image transmission, an encryption algorithm based on filling curve and adjacent pixel bit scrambling is proposed. Firstly, a new filling curve is designed and used to scramble image pixels globally. Secondly, the chaotic sequences are taken as the starting point and step length of Josephus traversal, and the adjacent pixels are bit scrambled by the improved Josephus traversal method. Through double scrambling, the high correlation between pixels of the plain image is broken. Finally, the security of the method is further improved by two-way ciphertext feedback. In addition, an adaptive key generation method associated with the plain image is designed to overcome the chosen/known-plaintext attack. The proposed scheme is analyzed from the aspects of key-space, key sensitivity, information entropy and correlations. The results show that this algorithm has good performance and sufficient security.
To improve the security of image transmission, an encryption algorithm based on filling curve and adjacent pixel bit scrambling is proposed. Firstly, a new filling curve is designed and used to scramble image pixels globally. Secondly, the chaotic sequences are taken as the starting point and step length of Josephus traversal, and the adjacent pixels are bit scrambled by the improved Josephus traversal method. Through double scrambling, the high correlation between pixels of the plain image is broken. Finally, the security of the method is further improved by two-way ciphertext feedback. In addition, an adaptive key generation method associated with the plain image is designed to overcome the chosen/known-plaintext attack. The proposed scheme is analyzed from the aspects of key-space, key sensitivity, information entropy and correlations. The results show that this algorithm has good performance and sufficient security.
