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2022 Vol. 44, No. 7
Display Method:
2022, 44(7): 1-4.
Abstract:
2022, 44(7): 2245-2252.
doi: 10.11999/JEIT211611
Abstract:
The secure transmission for Reconfigurable Intelligent Surface (RIS) assisted wireless communication systems is investigated in this paper. In the studied model, one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point. Therefore, it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem. This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined via jointly optimizing the RIS reflection reflector as well as the number of RIS elements, which results in a non-convex optimization problem that is intractable to solve by the traditional methods. To tackle this issue, a new algorithm by leveraging the advance of the emerging Deep Learning (DL) technique is proposed so as to find the near optimal RIS reflection vector and determine the near optimal number of RIS reflection elements. Simulation results show that the proposed method reaches 96% of the near optimal secure energy efficiency of the genie-aided algorithm.
The secure transmission for Reconfigurable Intelligent Surface (RIS) assisted wireless communication systems is investigated in this paper. In the studied model, one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point. Therefore, it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem. This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined via jointly optimizing the RIS reflection reflector as well as the number of RIS elements, which results in a non-convex optimization problem that is intractable to solve by the traditional methods. To tackle this issue, a new algorithm by leveraging the advance of the emerging Deep Learning (DL) technique is proposed so as to find the near optimal RIS reflection vector and determine the near optimal number of RIS reflection elements. Simulation results show that the proposed method reaches 96% of the near optimal secure energy efficiency of the genie-aided algorithm.
2022, 44(7): 2253-2263.
doi: 10.11999/JEIT211537
Abstract:
To resolve the problems of the effect of channel uncertainties, information leakage of users and energy-efficient improvement, a robust resource allocation algorithm for Reconfigurable Intelligent Surface (RIS)-assisted multiple-input single-output systems with eavesdroppers and imperfect channel state information is proposed. Firstly, considering the constraints of the minimum received power of energy-harvesting devices, the minimum secure rate of each legitimate user, the maximum transmit power of the base station and the phase-shift matrix of RIS, a multi-variable coupling and nonlinear resource optimization problem is formulated by jointly optimizing the information beamforming, energy beamforming and the phase shifts under bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex one by using Dinkelbach’s method, S-Procedure and the alternating optimization method. An alternating optimization algorithm based on successive convex approximation is proposed to solve the problem. Simulation results show that the proposed algorithm has lower outage probabilities by comparing it with the traditional non-robust algorithms.
To resolve the problems of the effect of channel uncertainties, information leakage of users and energy-efficient improvement, a robust resource allocation algorithm for Reconfigurable Intelligent Surface (RIS)-assisted multiple-input single-output systems with eavesdroppers and imperfect channel state information is proposed. Firstly, considering the constraints of the minimum received power of energy-harvesting devices, the minimum secure rate of each legitimate user, the maximum transmit power of the base station and the phase-shift matrix of RIS, a multi-variable coupling and nonlinear resource optimization problem is formulated by jointly optimizing the information beamforming, energy beamforming and the phase shifts under bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex one by using Dinkelbach’s method, S-Procedure and the alternating optimization method. An alternating optimization algorithm based on successive convex approximation is proposed to solve the problem. Simulation results show that the proposed algorithm has lower outage probabilities by comparing it with the traditional non-robust algorithms.
2022, 44(7): 2264-2272.
doi: 10.11999/JEIT210442
Abstract:
Recently, there is a growing interest in employing Intelligent Reflecting Surface (IRS) to assist physical layer key generation in wireless networks. However, current works only study the IRS-aided key generation schemes in Single-Input-Single-Output (SISO) communications, which have relatively low promotion of key generation rate. To fill this gap, the IRS-aided Multiple-Input-Multiple-Output (MIMO) key generation scheme is investigated in this paper. By fully exploring the reflection signals at the IRS as the artificial randomness, combined with the MIMO incident signals to build the fast channel, the performance of Key Generate Rate (KGR) can be significantly improved. The information theoretical security and limits of KGR are fully proven, and upper bound expression of KGR performance is derived. Based on this, the security under different eavesdropping attack scenarios are studied, where the eavesdropping attack is launched close to the legitimate transmitter/receiver, and IRS, respectively. It verifies that the proposed scheme improves significantly the KGR and security performance. Finally, the simulations are conducted to verify the proposed scheme and the correctness of theoretical analysis.
Recently, there is a growing interest in employing Intelligent Reflecting Surface (IRS) to assist physical layer key generation in wireless networks. However, current works only study the IRS-aided key generation schemes in Single-Input-Single-Output (SISO) communications, which have relatively low promotion of key generation rate. To fill this gap, the IRS-aided Multiple-Input-Multiple-Output (MIMO) key generation scheme is investigated in this paper. By fully exploring the reflection signals at the IRS as the artificial randomness, combined with the MIMO incident signals to build the fast channel, the performance of Key Generate Rate (KGR) can be significantly improved. The information theoretical security and limits of KGR are fully proven, and upper bound expression of KGR performance is derived. Based on this, the security under different eavesdropping attack scenarios are studied, where the eavesdropping attack is launched close to the legitimate transmitter/receiver, and IRS, respectively. It verifies that the proposed scheme improves significantly the KGR and security performance. Finally, the simulations are conducted to verify the proposed scheme and the correctness of theoretical analysis.
2022, 44(7): 2273-2280.
doi: 10.11999/JEIT211379
Abstract:
To improve the security of wireless transmission system and solve the problem that wireless channel is easily blocked by obstacles, a secure communication method based on an Intelligent Reflecting Surface (IRS) assisted Unmanned Air Vehicle (UAV) relay system is proposed. In the proposed method, the minimum-secrecy are maximized by jointly optimizing the position of UAV, beamforming at base station and phase shifts of IRS. To solve this complicated non-convex optimization problem, the original problem is divided into UAV position optimization sub-problem, beamforming and IRS’s phase shifts optimization sub-problem. The first order Taylor expansion method is applied to handle the non-convex terms of the optimization problems. Then, an alternate optimization algorithm is proposed to solve the problem. The simulation results shows that the proposed algorithm can improve the minimum-secrecy rate, and its convergence is well.
To improve the security of wireless transmission system and solve the problem that wireless channel is easily blocked by obstacles, a secure communication method based on an Intelligent Reflecting Surface (IRS) assisted Unmanned Air Vehicle (UAV) relay system is proposed. In the proposed method, the minimum-secrecy are maximized by jointly optimizing the position of UAV, beamforming at base station and phase shifts of IRS. To solve this complicated non-convex optimization problem, the original problem is divided into UAV position optimization sub-problem, beamforming and IRS’s phase shifts optimization sub-problem. The first order Taylor expansion method is applied to handle the non-convex terms of the optimization problems. Then, an alternate optimization algorithm is proposed to solve the problem. The simulation results shows that the proposed algorithm can improve the minimum-secrecy rate, and its convergence is well.
2022, 44(7): 2281-2288.
doi: 10.11999/JEIT211602
Abstract:
In this paper, a low complexity channel estimation algorithm is proposed, which is used to reduce the computational complexity of the millimeter wave channel estimation in the massive MIMO systems assisted by the Reconfigurable Intelligent Surfaces (RIS). In the proposed scheme, some elements of the RIS are connected to the Radio Frequency (RF) chain to estimate the channel between the base station/user and the RIS separately, which improves the flexibility of channel estimation. The zero-padding two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for angle estimation in this scenario for the first time. The path gain estimation is obtained by using the spectral peak of the two-dimensional spatial spectrum of the signal and its corresponding argument. Simulation results show that the proposed algorithm achieves excellent channel estimation performance, and based on the system parameter setting to ensure the channel estimation performance, the proposed algorithm has a strong complexity advantage.
In this paper, a low complexity channel estimation algorithm is proposed, which is used to reduce the computational complexity of the millimeter wave channel estimation in the massive MIMO systems assisted by the Reconfigurable Intelligent Surfaces (RIS). In the proposed scheme, some elements of the RIS are connected to the Radio Frequency (RF) chain to estimate the channel between the base station/user and the RIS separately, which improves the flexibility of channel estimation. The zero-padding two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for angle estimation in this scenario for the first time. The path gain estimation is obtained by using the spectral peak of the two-dimensional spatial spectrum of the signal and its corresponding argument. Simulation results show that the proposed algorithm achieves excellent channel estimation performance, and based on the system parameter setting to ensure the channel estimation performance, the proposed algorithm has a strong complexity advantage.
2022, 44(7): 2289-2298.
doi: 10.11999/JEIT220397
Abstract:
In order to enhance the transmission performance of Ultra-Reliable and Low-Latency Communication (URLLC) services in small cell in heterogeneous network scenarios, this paper proposes a beamforming algorithm based on Intelligent Reflecting Surface (IRS) assisted communication network to maximize users sum rates. Small cells in heterogeneous networks adopt short packet communication technology. On the premise of ensuring the communication quality of macro cell users, IRS is used to improve the short packet transmission performance of micro cell users under a certain decoding error probability, and a joint optimization beamforming vector and IRS are established. A problem is modeled for jointly optimizing beamforming vector and IRS phase shift vector. The non-convex optimization problem is split into two sub-problems by alternately fixing the optimization variables. The original problem is transformed into a convex optimization problem by using the Successive Convex Approximation (SCA) method, and the problem is solved by the alternating optimization algorithm. The simulation results show that the algorithm can effectively reduce the interference to small cell users in heterogeneous scenarios by deploying IRS, because the deployment of IRS can effectively optimize the beamforming vector and improve the short-packet transmission performance of small cell users, and the communication of IRS is enhanced. The effect is directly related to the decoding error probability of small cell users and the number of IRS reflection units.
In order to enhance the transmission performance of Ultra-Reliable and Low-Latency Communication (URLLC) services in small cell in heterogeneous network scenarios, this paper proposes a beamforming algorithm based on Intelligent Reflecting Surface (IRS) assisted communication network to maximize users sum rates. Small cells in heterogeneous networks adopt short packet communication technology. On the premise of ensuring the communication quality of macro cell users, IRS is used to improve the short packet transmission performance of micro cell users under a certain decoding error probability, and a joint optimization beamforming vector and IRS are established. A problem is modeled for jointly optimizing beamforming vector and IRS phase shift vector. The non-convex optimization problem is split into two sub-problems by alternately fixing the optimization variables. The original problem is transformed into a convex optimization problem by using the Successive Convex Approximation (SCA) method, and the problem is solved by the alternating optimization algorithm. The simulation results show that the algorithm can effectively reduce the interference to small cell users in heterogeneous scenarios by deploying IRS, because the deployment of IRS can effectively optimize the beamforming vector and improve the short-packet transmission performance of small cell users, and the communication of IRS is enhanced. The effect is directly related to the decoding error probability of small cell users and the number of IRS reflection units.
2022, 44(7): 2299-2308.
doi: 10.11999/JEIT220405
Abstract:
This paper investigates the optimization problem of physical layer security for an Intelligent Reflecting Surface (IRS) assisted multi-user downlink system. In each time slot, the information is sent to one user and is kept confidential from to other users, so it is a secure transmission system with multiple eavesdroppers. The target user of the information is the legitimate receiver and the others are regarded as eavesdroppers. The Channel State Information (CSI) of the eavesdropping channels owned by the base station is outdated because of the time variability of the channels, and there is error between the outdated CSI and the real CSI. To maximize system security rate in the worst case, the beamforming vectors of the signal and the artificial noise, and the IRS phase shifts are jointly optimized. The original optimization problem is a non-convex positive semi-definite programming problem.The problem is transformed into a convex problem and solved by using slack variables, penalty-based, Charnes-Cooper transformation, alternating optimization and other methods. The simulation results show that the proposed optimization algorithm can effectively improve the system security rate compared with other benchmark schemes.
This paper investigates the optimization problem of physical layer security for an Intelligent Reflecting Surface (IRS) assisted multi-user downlink system. In each time slot, the information is sent to one user and is kept confidential from to other users, so it is a secure transmission system with multiple eavesdroppers. The target user of the information is the legitimate receiver and the others are regarded as eavesdroppers. The Channel State Information (CSI) of the eavesdropping channels owned by the base station is outdated because of the time variability of the channels, and there is error between the outdated CSI and the real CSI. To maximize system security rate in the worst case, the beamforming vectors of the signal and the artificial noise, and the IRS phase shifts are jointly optimized. The original optimization problem is a non-convex positive semi-definite programming problem.The problem is transformed into a convex problem and solved by using slack variables, penalty-based, Charnes-Cooper transformation, alternating optimization and other methods. The simulation results show that the proposed optimization algorithm can effectively improve the system security rate compared with other benchmark schemes.
2022, 44(7): 2309-2316.
doi: 10.11999/JEIT211595
Abstract:
In order to compensate the performance loss caused by obstacle blocking in Mobile Edge Computing (MEC) system, a partial task offloading framework supported by Reconfigurable Intelligent Surface (RIS) is proposed. Firstly, the influence of the reflection between double-RIS on channel gain is analyzed. Then, a non-convex and multivariable coupling problem for minimization of total energy consumption of all users is formulated by the joint design of the transmit power of users, the offloading rate of users, the amount of offloading task of users, the time slot and the phase shift of RISs. To solve this problem, the alternating optimization technique is invoked to decouple the original non-convex problem into two subproblems which are solved by leveraging the Dinkelbach method and optimally conditions. Numerical results demonstrate that the proposed algorithm converges swiftly and reduces effectively the system energy consumption.
In order to compensate the performance loss caused by obstacle blocking in Mobile Edge Computing (MEC) system, a partial task offloading framework supported by Reconfigurable Intelligent Surface (RIS) is proposed. Firstly, the influence of the reflection between double-RIS on channel gain is analyzed. Then, a non-convex and multivariable coupling problem for minimization of total energy consumption of all users is formulated by the joint design of the transmit power of users, the offloading rate of users, the amount of offloading task of users, the time slot and the phase shift of RISs. To solve this problem, the alternating optimization technique is invoked to decouple the original non-convex problem into two subproblems which are solved by leveraging the Dinkelbach method and optimally conditions. Numerical results demonstrate that the proposed algorithm converges swiftly and reduces effectively the system energy consumption.
2022, 44(7): 2317-2324.
doi: 10.11999/JEIT210714
Abstract:
To resolve the effect of channel uncertainties and low energy transfer efficiency caused by obstacles, a robust Energy Efficiency (EE) maximization algorithm for an Intelligent Reflecting Surface (IRS)-assisted Wireless-Powered Communication Network (WOCN) is proposed. Firstly, considering the constraint of the minimum energy harvesting, the constraint of the phase-shift, and the constraint of the minimum throughput, a multi-variable coupling nonlinear resource allocation model that jointly optimizing the energy beamforming, the phase shifts, and the transmission time is established based on the bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex optimization problem by using the worst-case approach, the variable substitution and S-Procedure methods. At the same time, a robust EE maximization algorithm based on iteration is proposed to solve the problem. The simulation results show that the proposed algorithm has better EE and robustness by comparing it with the existing algorithms.
To resolve the effect of channel uncertainties and low energy transfer efficiency caused by obstacles, a robust Energy Efficiency (EE) maximization algorithm for an Intelligent Reflecting Surface (IRS)-assisted Wireless-Powered Communication Network (WOCN) is proposed. Firstly, considering the constraint of the minimum energy harvesting, the constraint of the phase-shift, and the constraint of the minimum throughput, a multi-variable coupling nonlinear resource allocation model that jointly optimizing the energy beamforming, the phase shifts, and the transmission time is established based on the bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex optimization problem by using the worst-case approach, the variable substitution and S-Procedure methods. At the same time, a robust EE maximization algorithm based on iteration is proposed to solve the problem. The simulation results show that the proposed algorithm has better EE and robustness by comparing it with the existing algorithms.
2022, 44(7): 2325-2331.
doi: 10.11999/JEIT220195
Abstract:
In order to mitigate the adverse effect of blockages between the Unmanned Aerial Vehicle (UAV) and Ground Users (GUs), a throughput maximization algorithm for an Intelligent Reflecting Surface (IRS)-aided UAV communication network is proposed. First, considering the constraints of the energy causality, the IRS phase-shift, the UAV mobility, etc, a multi-variable coupling optimization problem is proposed with jointly optimizing the phase-shift of the IRS, the resource allocation of GUs, and the UAV trajectory. Second, the original non-convex problem is decomposed into three simpler sub-problems via the Block Coordinate Descent (BCD), which are tackled by the triangle inequality, introducing the slack variables and Successive Convex Approximation (SCA). Numerical results show that the proposed algorithm has a desirable convergence, as well as improves effectively the system sum-throughput.
In order to mitigate the adverse effect of blockages between the Unmanned Aerial Vehicle (UAV) and Ground Users (GUs), a throughput maximization algorithm for an Intelligent Reflecting Surface (IRS)-aided UAV communication network is proposed. First, considering the constraints of the energy causality, the IRS phase-shift, the UAV mobility, etc, a multi-variable coupling optimization problem is proposed with jointly optimizing the phase-shift of the IRS, the resource allocation of GUs, and the UAV trajectory. Second, the original non-convex problem is decomposed into three simpler sub-problems via the Block Coordinate Descent (BCD), which are tackled by the triangle inequality, introducing the slack variables and Successive Convex Approximation (SCA). Numerical results show that the proposed algorithm has a desirable convergence, as well as improves effectively the system sum-throughput.
2022, 44(7): 2332-2341.
doi: 10.11999/JEIT210521
Abstract:
To improve the robustness and Energy Efficiency (EE) of Non-Orthogonal Multiple Access (NOMA)-based networks, a robust EE maximization-based algorithm is proposed in a Reconfigurable Intelligent Surface (RIS)-assisted NOMA network with imperfect channel state information. Considering the outage probability constraints of users' Signal-to-Interference-to-Noise Ratio (SINR), the maximum transmit power constraints of the base station, and continuous phase shift constraints, a nonlinear EE maximization-based resource allocation model is established. By using Dinkelbach's method the fractional objective function is converted into a linear parameter subtraction form, the S-procedure method is used to transform the outage probability of SINR with channel uncertainty into deterministic form. By using the alternative optimization method, the non-convex optimization problem is converted into several convex optimization subproblems, then the CVX is used to solve the subproblems. Simulation results show that the proposed algorithm is 7.4% higher than the without Reconfigurable Intelligent Surface (RIS) algorithm in terms of EE, the proposed algorithm is 85.5% lower than the non-robust algorithm in terms of the outage probability of SINR.
To improve the robustness and Energy Efficiency (EE) of Non-Orthogonal Multiple Access (NOMA)-based networks, a robust EE maximization-based algorithm is proposed in a Reconfigurable Intelligent Surface (RIS)-assisted NOMA network with imperfect channel state information. Considering the outage probability constraints of users' Signal-to-Interference-to-Noise Ratio (SINR), the maximum transmit power constraints of the base station, and continuous phase shift constraints, a nonlinear EE maximization-based resource allocation model is established. By using Dinkelbach's method the fractional objective function is converted into a linear parameter subtraction form, the S-procedure method is used to transform the outage probability of SINR with channel uncertainty into deterministic form. By using the alternative optimization method, the non-convex optimization problem is converted into several convex optimization subproblems, then the CVX is used to solve the subproblems. Simulation results show that the proposed algorithm is 7.4% higher than the without Reconfigurable Intelligent Surface (RIS) algorithm in terms of EE, the proposed algorithm is 85.5% lower than the non-robust algorithm in terms of the outage probability of SINR.
2022, 44(7): 2342-2348.
doi: 10.11999/JEIT220072
Abstract:
Reconfigurable Intelligent Surface (RIS), which can intelligently change the wireless propagation environment to improve significantly communication performance, has been regarded as one of the potential key technologies of 6G. In order to enhance further the performance of RIS-assisted communication system, a two-way RIS selection communication scheme is proposed in this paper, and the transmission efficiency of the system is effectively improved by introducing full-duplex technology and self-interference cancellation technology. The closed-form expression of the outage probability of the proposed system is derived under Rayleigh fading channel, and the relationship among the outage probability, the number of RIS reflecting elements and the number of RIS in the system is obtained. Finally, the accuracy of the derivation and the performance advantages of the proposed scheme are verified by Monte Carlo simulations.
Reconfigurable Intelligent Surface (RIS), which can intelligently change the wireless propagation environment to improve significantly communication performance, has been regarded as one of the potential key technologies of 6G. In order to enhance further the performance of RIS-assisted communication system, a two-way RIS selection communication scheme is proposed in this paper, and the transmission efficiency of the system is effectively improved by introducing full-duplex technology and self-interference cancellation technology. The closed-form expression of the outage probability of the proposed system is derived under Rayleigh fading channel, and the relationship among the outage probability, the number of RIS reflecting elements and the number of RIS in the system is obtained. Finally, the accuracy of the derivation and the performance advantages of the proposed scheme are verified by Monte Carlo simulations.
2022, 44(7): 2349-2357.
doi: 10.11999/JEIT210976
Abstract:
To mitigate the effects of shadow fading and obstacle blocking, Intelligent Reflecting Surface (IRS) has become an effective technology to improve Energy Efficiency (EE) and reduce hardware cost of wireless communication systems. However, traditional radio Resource Allocation (RA) algorithms have ignored the impact of Hardware Impairments (HIs) of system’s transceivers. Since the distorted received signals are caused by the nonlinearity of amplifiers and the influence of phase noise. so that this type of algorithm can degrade system performance. To deal with this issue, Hardware Impairments of the transceiver and the influence of eavesdroppers is considered, and the problem of energy-saving optimization of hardware impairment in IRS-assisted secure communication systems is investigated. Firstly, an EE-based maximization resource optimization problem is formulated under the maximum transmit power constraint of the base station and the minimum secure rate constraints of users. Secondly, the original non-convex problem is transformed into a convex problem by using the auxiliary variable substitution, semidefinite relaxation and Dinkelbach’s method. Finally, simulation results show that the proposed algorithm is improved 8.3% in terms of security EE and is reduced by 43.5% in terms of the outage probability of legitimate users by comparing it with the traditional RA algorithms without HIs. Therefore, the proposed algorithm has better security and hardware damage resistance.
To mitigate the effects of shadow fading and obstacle blocking, Intelligent Reflecting Surface (IRS) has become an effective technology to improve Energy Efficiency (EE) and reduce hardware cost of wireless communication systems. However, traditional radio Resource Allocation (RA) algorithms have ignored the impact of Hardware Impairments (HIs) of system’s transceivers. Since the distorted received signals are caused by the nonlinearity of amplifiers and the influence of phase noise. so that this type of algorithm can degrade system performance. To deal with this issue, Hardware Impairments of the transceiver and the influence of eavesdroppers is considered, and the problem of energy-saving optimization of hardware impairment in IRS-assisted secure communication systems is investigated. Firstly, an EE-based maximization resource optimization problem is formulated under the maximum transmit power constraint of the base station and the minimum secure rate constraints of users. Secondly, the original non-convex problem is transformed into a convex problem by using the auxiliary variable substitution, semidefinite relaxation and Dinkelbach’s method. Finally, simulation results show that the proposed algorithm is improved 8.3% in terms of security EE and is reduced by 43.5% in terms of the outage probability of legitimate users by comparing it with the traditional RA algorithms without HIs. Therefore, the proposed algorithm has better security and hardware damage resistance.
2022, 44(7): 2358-2365.
doi: 10.11999/JEIT211389
Abstract:
Equivalent channel time variation caused by coefficient conversion of reflective elements in Zero Prefix Orthogonal Frequency Division Multiplexing (ZP-OFDM) system in Reconfigurable Intelligent Surface (RIS) scene, it destroys the orthogonality of Orthogonal Frequency Division Multiplexing (OFDM) system and produces serious Inter Carrier Interference (ICI). In this paper, by constructing the system transmission model in this scenario, analyzing the ICI power and modeling the time-varying characteristics of the coefficient conversion of the reflection element, the ICI is compensated by constructing the inter subcarrier interference suppression matrix to suppress the impact of the equivalent time-varying channel caused by the change of reflection coefficient on the system performance. The simulation results show that the inter subcarrier interference is effectively suppressed, and the interference suppression algorithm proposed in this paper can significantly improve the transmission performance of the system.
Equivalent channel time variation caused by coefficient conversion of reflective elements in Zero Prefix Orthogonal Frequency Division Multiplexing (ZP-OFDM) system in Reconfigurable Intelligent Surface (RIS) scene, it destroys the orthogonality of Orthogonal Frequency Division Multiplexing (OFDM) system and produces serious Inter Carrier Interference (ICI). In this paper, by constructing the system transmission model in this scenario, analyzing the ICI power and modeling the time-varying characteristics of the coefficient conversion of the reflection element, the ICI is compensated by constructing the inter subcarrier interference suppression matrix to suppress the impact of the equivalent time-varying channel caused by the change of reflection coefficient on the system performance. The simulation results show that the inter subcarrier interference is effectively suppressed, and the interference suppression algorithm proposed in this paper can significantly improve the transmission performance of the system.
2022, 44(7): 2366-2373.
doi: 10.11999/JEIT211255
Abstract:
To reduce the influence of eavesdropper on the information reception of the legitimate receiver and the amount of useful information received by the eavesdropper, the beam cancellation at the eavesdropper in Wireless Powered Communication Network (WPCN)with Intelligent Reflecting Surface (IRS) is studied in this paper. Firstly, eavesdropping detection is carried out together with energy transmission, IRS chooses whether to adopt anti-eavesdropping mode according to the energy state of legitimate users. Then, under the conditions of eavesdropping, the maximization of system secrecy rate is studied for perfect and imperfect eavesdropping channel respectively. The maximization algorithm is a multi-variable coupling non-convex problem composing of phase shift and power distribution and time distribution, which is solved by methods such as stepwise optimization, variable substitution and S-Lemma. The simulation results show that, compared with the benchmark algorithm, the proposed algorithm improves the security rate. When the number of IRS elements is 80, the security rate is increased by 44%, and when the transmit power is 40 dBm, the security rate is increased by 34%.
To reduce the influence of eavesdropper on the information reception of the legitimate receiver and the amount of useful information received by the eavesdropper, the beam cancellation at the eavesdropper in Wireless Powered Communication Network (WPCN)with Intelligent Reflecting Surface (IRS) is studied in this paper. Firstly, eavesdropping detection is carried out together with energy transmission, IRS chooses whether to adopt anti-eavesdropping mode according to the energy state of legitimate users. Then, under the conditions of eavesdropping, the maximization of system secrecy rate is studied for perfect and imperfect eavesdropping channel respectively. The maximization algorithm is a multi-variable coupling non-convex problem composing of phase shift and power distribution and time distribution, which is solved by methods such as stepwise optimization, variable substitution and S-Lemma. The simulation results show that, compared with the benchmark algorithm, the proposed algorithm improves the security rate. When the number of IRS elements is 80, the security rate is increased by 44%, and when the transmit power is 40 dBm, the security rate is increased by 34%.
2022, 44(7): 2374-2381.
doi: 10.11999/JEIT220068
Abstract:
Reconfigurable Intelligent Surface (RIS) breaks technological limitations of traditional wireless communication systems, it creates new opportunities for development of 5G-Adv and 6G by introducing reconfigurable communication environment. RIS system architecture and working principle are introduced in detail, including hardware design and beamforming method. RIS assisted wireless communication system is introduced in-depth, received signal gain and system performance are analyzed in details. Combined with indoor test, beamforming ability of RIS is verified.
Reconfigurable Intelligent Surface (RIS) breaks technological limitations of traditional wireless communication systems, it creates new opportunities for development of 5G-Adv and 6G by introducing reconfigurable communication environment. RIS system architecture and working principle are introduced in detail, including hardware design and beamforming method. RIS assisted wireless communication system is introduced in-depth, received signal gain and system performance are analyzed in details. Combined with indoor test, beamforming ability of RIS is verified.
2022, 44(7): 2382-2391.
doi: 10.11999/JEIT211468
Abstract:
For the higher requirements of communication quality and spectrum efficiency in future wireless mobile communication systems, this paper combines Index Modulation (IM) and Reconfigurable Intelligent Surface (RIS) technologies to establish the RIS-assisted Single-Input Multiple-Output (SIMO) communication systems with IM, and the Variational Bayes Inference (VBI)-based signal detection algorithm is proposed. Firstly, in the system, the RIS units are divided into a number of sub-blocks, and the activation state of the RIS sub-block is used to transmit additional information. Then, the VBI is used to calculate the approximate posterior distribution of the phase shift vector corresponding to the activated RIS sub-block and the signal vector for detection. Finally, the index information bits are recovered by using the logarithmic zero gradient value of the approximate posterior distribution of the RIS phase shift vector combined with the Orthogonal Matching Pursuit (OMP) algorithm, and taking advantage of the logarithmic zero gradient value of the signal to be detected, the transmitted symbols are recovered. Meanwhile, the system average rate is theoretically deduced. The simulation results show that compared with the traditional RIS-assisted SIMO communication systems, the RIS -assisted SIMO systems with IM has a higher system average rate; and in contrast to existing algorithms, the proposed one has lower bit errors rate.
For the higher requirements of communication quality and spectrum efficiency in future wireless mobile communication systems, this paper combines Index Modulation (IM) and Reconfigurable Intelligent Surface (RIS) technologies to establish the RIS-assisted Single-Input Multiple-Output (SIMO) communication systems with IM, and the Variational Bayes Inference (VBI)-based signal detection algorithm is proposed. Firstly, in the system, the RIS units are divided into a number of sub-blocks, and the activation state of the RIS sub-block is used to transmit additional information. Then, the VBI is used to calculate the approximate posterior distribution of the phase shift vector corresponding to the activated RIS sub-block and the signal vector for detection. Finally, the index information bits are recovered by using the logarithmic zero gradient value of the approximate posterior distribution of the RIS phase shift vector combined with the Orthogonal Matching Pursuit (OMP) algorithm, and taking advantage of the logarithmic zero gradient value of the signal to be detected, the transmitted symbols are recovered. Meanwhile, the system average rate is theoretically deduced. The simulation results show that compared with the traditional RIS-assisted SIMO communication systems, the RIS -assisted SIMO systems with IM has a higher system average rate; and in contrast to existing algorithms, the proposed one has lower bit errors rate.
2022, 44(7): 2392-2399.
doi: 10.11999/JEIT211618
Abstract:
In this work, an Intelligent Reflecting Surface (IRS) aided and Artificial Noise (AN) enhanced covert wireless communications is considered to improve the covert transmission performance. Firstly, the detection performance at Willie is analyzed and a lower bound on Willie’s minimum total detection error probability is presented. On this basis, an optimization problem that maximizes the effective throughput subject to the covertness constraint and the maximum AN transmit power constraint is formulated. The optimization problem is non-convex, which is generally difficult to tackle directly. Then, an alternating iterative algorithm based on Dinkelbach method is proposed to jointly design the IRS reflection beamforming and Alice’s transmit power together with Bob’s AN transmit power. In order to reduce the computational complexity, a low-complexity algorithm is further proposed to obtain analytical expressions for the corresponding optimization variables. Simulation results show that the proposed scheme improves significantly the covert transmission performance compared with the schemes without IRS and without AN.
In this work, an Intelligent Reflecting Surface (IRS) aided and Artificial Noise (AN) enhanced covert wireless communications is considered to improve the covert transmission performance. Firstly, the detection performance at Willie is analyzed and a lower bound on Willie’s minimum total detection error probability is presented. On this basis, an optimization problem that maximizes the effective throughput subject to the covertness constraint and the maximum AN transmit power constraint is formulated. The optimization problem is non-convex, which is generally difficult to tackle directly. Then, an alternating iterative algorithm based on Dinkelbach method is proposed to jointly design the IRS reflection beamforming and Alice’s transmit power together with Bob’s AN transmit power. In order to reduce the computational complexity, a low-complexity algorithm is further proposed to obtain analytical expressions for the corresponding optimization variables. Simulation results show that the proposed scheme improves significantly the covert transmission performance compared with the schemes without IRS and without AN.
2022, 44(7): 2400-2406.
doi: 10.11999/JEIT211271
Abstract:
Millimeter-wave is a typical line-of-sight transmission method, which is seriously affected by atmospheric absorption. Aiming at the limited non-line-of-sight propagation of millimeter waves, Intelligent Reflecting Surface (IRS) is used to assist millimeter-wave communications, and the Khatri-Rao product combined with the Vector Approximate Message Passing (KR-VAMP) algorithm is proposed, which can improve the channel estimation quality of the millimeter-wave communication systems. By adopting the Khatri-Rao product, the cascaded channel problem is transformed into a sparse signal recovery problem. The proposed algorithm combines with the advantages of the VAMP’s vector and iterative threshold algorithm. The number of training iterations and the channel estimation error are reduced in the IRS-assisted millimeter-wave system. Finally, based on simulation results, the influence of each variable on the Mean Square Error (MMSE) of channel estimation and the convergence of MMSE with the number of iterations are analyzed. It also verifies that the algorithm has better performance than other Approximate Message Passing (AMP) algorithms.
Millimeter-wave is a typical line-of-sight transmission method, which is seriously affected by atmospheric absorption. Aiming at the limited non-line-of-sight propagation of millimeter waves, Intelligent Reflecting Surface (IRS) is used to assist millimeter-wave communications, and the Khatri-Rao product combined with the Vector Approximate Message Passing (KR-VAMP) algorithm is proposed, which can improve the channel estimation quality of the millimeter-wave communication systems. By adopting the Khatri-Rao product, the cascaded channel problem is transformed into a sparse signal recovery problem. The proposed algorithm combines with the advantages of the VAMP’s vector and iterative threshold algorithm. The number of training iterations and the channel estimation error are reduced in the IRS-assisted millimeter-wave system. Finally, based on simulation results, the influence of each variable on the Mean Square Error (MMSE) of channel estimation and the convergence of MMSE with the number of iterations are analyzed. It also verifies that the algorithm has better performance than other Approximate Message Passing (AMP) algorithms.
2022, 44(7): 2407-2415.
doi: 10.11999/JEIT211613
Abstract:
In this paper, the optimization problem of the 3D trajectory for Unmanned Aerial Vehicle (UAV) assisted by Reconfigurable Intelligent Surface (RIS) in physical layer security is studied. Specifically, when the RIS assisted UAV transmits wirelessly information to the ground user, the physical layer security rate is maximized by jointly optimizing the RIS phase shift and the UAV's 3D trajectory. However, because the objective function is non convex, the traditional optimization technology is difficult to solve it directly. The dynamic and complex optimization problems in wireless communication can be solved by deep reinforcement learning. Based on reinforcement learning Double Deep Q Network (DDQN), a joint optimization algorithm of RIS phase shift and UAV 3D trajectory is designed in this paper to maximize the achievable average safety rate. The simulation results show that the designed RIS assisted UAV communication optimization algorithm can obtain higher safety rate than the Successive Convex Approximation (SCA) algorithm with fixed flight altitude, RIS algorithm with random phase shift and algorithm without RIS.
In this paper, the optimization problem of the 3D trajectory for Unmanned Aerial Vehicle (UAV) assisted by Reconfigurable Intelligent Surface (RIS) in physical layer security is studied. Specifically, when the RIS assisted UAV transmits wirelessly information to the ground user, the physical layer security rate is maximized by jointly optimizing the RIS phase shift and the UAV's 3D trajectory. However, because the objective function is non convex, the traditional optimization technology is difficult to solve it directly. The dynamic and complex optimization problems in wireless communication can be solved by deep reinforcement learning. Based on reinforcement learning Double Deep Q Network (DDQN), a joint optimization algorithm of RIS phase shift and UAV 3D trajectory is designed in this paper to maximize the achievable average safety rate. The simulation results show that the designed RIS assisted UAV communication optimization algorithm can obtain higher safety rate than the Successive Convex Approximation (SCA) algorithm with fixed flight altitude, RIS algorithm with random phase shift and algorithm without RIS.
2022, 44(7): 2416-2424.
doi: 10.11999/JEIT220180
Abstract:
In order to meet the high computing demands caused by emerging compute-intensive applications in Mobile Edge Computing (MEC), this paper proposes a Digital Twin (DT)-empowered task offloading scheme where Reconfigurable Intelligent Surface (RIS) is used to enhance the communication links and extend the coverage. Firstly, the joint optimization of user offloading strategy, RIS phase-shift vector, beamforming vector, transmit power of users and computation capacity allocation are investigated with the aim of minimizing the total energy consumption of users and resource devices under the constraints of communication and computing resources. Then, the formulated non-convex combinational optimization problem is decomposed into three sub-problems, including RIS phase-shift design, binary optimization of transmit power, and computing resource allocation. In addition, the Double Deep Q Network (DDQN) approach is invoked to determine the offloading decisions and an alternating iteration optimization algorithm is designed to achieve the optimal solution. Simulation results show that the DDQN-based algorithm is able to train quickly and reduce effectively the total energy consumption of the system.
In order to meet the high computing demands caused by emerging compute-intensive applications in Mobile Edge Computing (MEC), this paper proposes a Digital Twin (DT)-empowered task offloading scheme where Reconfigurable Intelligent Surface (RIS) is used to enhance the communication links and extend the coverage. Firstly, the joint optimization of user offloading strategy, RIS phase-shift vector, beamforming vector, transmit power of users and computation capacity allocation are investigated with the aim of minimizing the total energy consumption of users and resource devices under the constraints of communication and computing resources. Then, the formulated non-convex combinational optimization problem is decomposed into three sub-problems, including RIS phase-shift design, binary optimization of transmit power, and computing resource allocation. In addition, the Double Deep Q Network (DDQN) approach is invoked to determine the offloading decisions and an alternating iteration optimization algorithm is designed to achieve the optimal solution. Simulation results show that the DDQN-based algorithm is able to train quickly and reduce effectively the total energy consumption of the system.
2022, 44(7): 2425-2430.
doi: 10.11999/JEIT211473
Abstract:
The Reconfigurable Intelligent Surface (RIS) is a new and cost-effective solution for achieving high energy efficiency through massive low-cost passive elements. In the far field case, a lot of work are carried out on the assumption that the center of RIS is the reflection point. In the case of multi-user far field, the difference of user positions will increase the power consumption of Base Station(BS). Users and RIS cells are matched by resorting to Kuhn-Munkres(KM) algorithm in this paper, taking the transmitting power of BS as cost matrix. Under the constraints of SNR, the transmitting power of BS can be reduced by the user assignment method. Simulation results show that the matching method between users and RIS units adopted in this paper can reduce BS power consumption by up to 1% compared with random units of RIS.
The Reconfigurable Intelligent Surface (RIS) is a new and cost-effective solution for achieving high energy efficiency through massive low-cost passive elements. In the far field case, a lot of work are carried out on the assumption that the center of RIS is the reflection point. In the case of multi-user far field, the difference of user positions will increase the power consumption of Base Station(BS). Users and RIS cells are matched by resorting to Kuhn-Munkres(KM) algorithm in this paper, taking the transmitting power of BS as cost matrix. Under the constraints of SNR, the transmitting power of BS can be reduced by the user assignment method. Simulation results show that the matching method between users and RIS units adopted in this paper can reduce BS power consumption by up to 1% compared with random units of RIS.
2022, 44(7): 2431-2439.
doi: 10.11999/JEIT210415
Abstract:
In Mobile Edge Computing (MEC) environment, deploying application services in the form of Virtual Machines (VM) at the edge of the network can effectively reduce the service response delay and reduce the data traffic of core network. There have been many solutions to the problem of optimal allocation of edge network resources, but few studies consider the optimal deployment of VM that provide users with multiple application services to mobile edge networks. To this end, two heuristic algorithms are proposed, Fitness-based Heuristic Placement Algorithm (FHPA) and Divide-and-Conquer Based Heuristic Placement Algorithm (DCBHPA). By distributing VMs that support multiple application services to the MEC network, these two algorithms aim to minimize the data traffic in MEC architecture. Besides, FHPA and DCBHPA define respectively different fitness computing models based on the network connection characteristics of edge servers, as well as the differences in users’ application requests. Thus, VM configuration can be realized through the sub-problem division mechanism. Compared with the baseline algorithms, the simulation results show that the proposed algorithms can better control the system data traffic and improve effectively the utility of edge network service resources.
In Mobile Edge Computing (MEC) environment, deploying application services in the form of Virtual Machines (VM) at the edge of the network can effectively reduce the service response delay and reduce the data traffic of core network. There have been many solutions to the problem of optimal allocation of edge network resources, but few studies consider the optimal deployment of VM that provide users with multiple application services to mobile edge networks. To this end, two heuristic algorithms are proposed, Fitness-based Heuristic Placement Algorithm (FHPA) and Divide-and-Conquer Based Heuristic Placement Algorithm (DCBHPA). By distributing VMs that support multiple application services to the MEC network, these two algorithms aim to minimize the data traffic in MEC architecture. Besides, FHPA and DCBHPA define respectively different fitness computing models based on the network connection characteristics of edge servers, as well as the differences in users’ application requests. Thus, VM configuration can be realized through the sub-problem division mechanism. Compared with the baseline algorithms, the simulation results show that the proposed algorithms can better control the system data traffic and improve effectively the utility of edge network service resources.
2022, 44(7): 2440-2448.
doi: 10.11999/JEIT210413
Abstract:
The current rapid growth of Internet of Things (IoT) applications is a huge challenge to the computing power of user equipment. The Fog Computing (FC) network can provide short-distance and fast computing services for user equipment, and provides a solution for user equipment with limited resources and limited computing capabilities. A blockchain-enabled fog network model is proposed, in which user equipment can offload computationally intensive tasks to nodes with strong computational capabilities. In order to minimize task processing delay and energy consumption, two task offloading models are introduced, namely Device-to-Device (D2D) collaboration group task offloading and Fog Nodes (FNs) task offloading. In addition, in view of the data security problem of the fog computing network task offloading process, blockchain technology is introduced to build a decentralized distributed ledger to prevent malicious nodes from modifying transaction information and achieve safe and reliable data transmission. In order to reduce the delay and energy consumption of the consensus mechanism, an improved voting-based Delegated-Proof-of-Stake (DPoS) consensus mechanism is proposed. FNs with votes exceeding the threshold form a verification set, and the FNs in the verification set take turns as the manager to generate new blocks. Finally, with the goal of minimizing the network cost, the task offloading decision, transmission rate allocation and computing resource allocation are jointly optimized, and the Task Offloading Decision and Resource Allocation (TODRA) algorithm is proposed to solve the problem. The effectiveness of the algorithm is verified by simulation experiments.
The current rapid growth of Internet of Things (IoT) applications is a huge challenge to the computing power of user equipment. The Fog Computing (FC) network can provide short-distance and fast computing services for user equipment, and provides a solution for user equipment with limited resources and limited computing capabilities. A blockchain-enabled fog network model is proposed, in which user equipment can offload computationally intensive tasks to nodes with strong computational capabilities. In order to minimize task processing delay and energy consumption, two task offloading models are introduced, namely Device-to-Device (D2D) collaboration group task offloading and Fog Nodes (FNs) task offloading. In addition, in view of the data security problem of the fog computing network task offloading process, blockchain technology is introduced to build a decentralized distributed ledger to prevent malicious nodes from modifying transaction information and achieve safe and reliable data transmission. In order to reduce the delay and energy consumption of the consensus mechanism, an improved voting-based Delegated-Proof-of-Stake (DPoS) consensus mechanism is proposed. FNs with votes exceeding the threshold form a verification set, and the FNs in the verification set take turns as the manager to generate new blocks. Finally, with the goal of minimizing the network cost, the task offloading decision, transmission rate allocation and computing resource allocation are jointly optimized, and the Task Offloading Decision and Resource Allocation (TODRA) algorithm is proposed to solve the problem. The effectiveness of the algorithm is verified by simulation experiments.
2022, 44(7): 2449-2460.
doi: 10.11999/JEIT210408
Abstract:
Considering the technical difficulty of radar to detect small targets embedded in the sea clutter, a three-feature fusion detection method based on diagonal integrated bispectrum is proposed. Firstly, the diagonal integrated bispectrum is obtained from the estimated bispectrum of the signal to be detected. Then, according to the nonlinear coupling difference between sea clutter cell and target cell, three features consist of peak value, centroid frequency and spectrum width are extracted from the diagonal integrated bispectrum. Considering that the number of coherent pulses used by radar in scanning mode is usually small, it is easy to lead to feature instability, and then affect the separability of sea clutter and target. For this reason, through the comprehensive application of multi-frame scanning historical data and current frame data, three cumulative features including cumulative peak value, total variation, cumulative spectrum width are obtained by accumulating three spectrum features. Finally, the convex hull classification algorithm is used to perform fusion detection in three dimensional feature space. The measured CSIR dataset verifies that, under same parameters, the proposed detection method has better detection performance compared with the existing detection methods based on three time-frequency features, amplitude feature and doppler features, fractal feature.
Considering the technical difficulty of radar to detect small targets embedded in the sea clutter, a three-feature fusion detection method based on diagonal integrated bispectrum is proposed. Firstly, the diagonal integrated bispectrum is obtained from the estimated bispectrum of the signal to be detected. Then, according to the nonlinear coupling difference between sea clutter cell and target cell, three features consist of peak value, centroid frequency and spectrum width are extracted from the diagonal integrated bispectrum. Considering that the number of coherent pulses used by radar in scanning mode is usually small, it is easy to lead to feature instability, and then affect the separability of sea clutter and target. For this reason, through the comprehensive application of multi-frame scanning historical data and current frame data, three cumulative features including cumulative peak value, total variation, cumulative spectrum width are obtained by accumulating three spectrum features. Finally, the convex hull classification algorithm is used to perform fusion detection in three dimensional feature space. The measured CSIR dataset verifies that, under same parameters, the proposed detection method has better detection performance compared with the existing detection methods based on three time-frequency features, amplitude feature and doppler features, fractal feature.
2022, 44(7): 2461-2468.
doi: 10.11999/JEIT210140
Abstract:
The Intermittent Frequency Modulation Continuous Wave (IFMCW) Synthetic Aperture Radar (SAR) mode solves the problem that the spaceborne Frequency Modulation Continuous Wave (FMCW) SAR must be bistatic by alternately transmitting and receiving signals in different time intervals. However, in this mode, the radar antenna will work intermittently in the transmitting and receiving state, resulting in periodical gaps in the echo data. In order to solve the above problems, a Missing-data Iterative Adaptive imaging processing Approach (MIAA) is proposed, based on Accumulated Aperture Interpolation Technique (AAIT) to recover the data gaps. Experimental results show that the proposed method can effectively recover missing data, thus improving significantly imaging quality and suppressing greatly the artifacts energy caused by the periodical data gaps.
The Intermittent Frequency Modulation Continuous Wave (IFMCW) Synthetic Aperture Radar (SAR) mode solves the problem that the spaceborne Frequency Modulation Continuous Wave (FMCW) SAR must be bistatic by alternately transmitting and receiving signals in different time intervals. However, in this mode, the radar antenna will work intermittently in the transmitting and receiving state, resulting in periodical gaps in the echo data. In order to solve the above problems, a Missing-data Iterative Adaptive imaging processing Approach (MIAA) is proposed, based on Accumulated Aperture Interpolation Technique (AAIT) to recover the data gaps. Experimental results show that the proposed method can effectively recover missing data, thus improving significantly imaging quality and suppressing greatly the artifacts energy caused by the periodical data gaps.
Signal Parameter Estimation Algorithm for Orthogonal Dipole Array Based on Finite Rate of Innovation
2022, 44(7): 2469-2477.
doi: 10.11999/JEIT210357
Abstract:
To deal with the grid mismatch problem of compressed sensing algorithms in polarization-sensitive array Direction Of Arrival (DOA) estimation, a gridless signal parameter estimation algorithm for orthogonal dipole array based on Finite Rate of Innovation (FRI) is proposed. First, two sub-arrays of the uniform orthogonal dipole linear array with the different antenna polarization direction, are used to obtain the sum of their self-correlation covariance matrix, and the covariance matrix satisfying the Toeplitz structure is recovered through the covariance fitting criteria. Then, the covariance matrix is used to construct the FRI signal reconstruction model, and the zeros of the polynomial with the reconstruction result as the coefficient is solved to obtain the estimation result of the DOA parameter of the incident signal. Finally, using the estimated DOA parameters and the self-correlation covariance matrix and cross-correlation covariance matrix of the two sub-arrays, the least square method is used to calculate the polarization parameter estimation results of the incident signal. Simulation experiments show that this algorithm has higher estimation accuracy and angle resolution compared with subspace and compressed sensing algorithms.
To deal with the grid mismatch problem of compressed sensing algorithms in polarization-sensitive array Direction Of Arrival (DOA) estimation, a gridless signal parameter estimation algorithm for orthogonal dipole array based on Finite Rate of Innovation (FRI) is proposed. First, two sub-arrays of the uniform orthogonal dipole linear array with the different antenna polarization direction, are used to obtain the sum of their self-correlation covariance matrix, and the covariance matrix satisfying the Toeplitz structure is recovered through the covariance fitting criteria. Then, the covariance matrix is used to construct the FRI signal reconstruction model, and the zeros of the polynomial with the reconstruction result as the coefficient is solved to obtain the estimation result of the DOA parameter of the incident signal. Finally, using the estimated DOA parameters and the self-correlation covariance matrix and cross-correlation covariance matrix of the two sub-arrays, the least square method is used to calculate the polarization parameter estimation results of the incident signal. Simulation experiments show that this algorithm has higher estimation accuracy and angle resolution compared with subspace and compressed sensing algorithms.
2022, 44(7): 2478-2487.
doi: 10.11999/JEIT210800
Abstract:
Considering the multi solution problem of minimum solution method for calibration of 2D lidar and camera, a calibration method based on the estimation of the effective lower bound of observation probability is proposed. Firstly, a hierarchical clustering method with minimum solution set is proposed which should be used to replace the original solution set with each kind of optimal solution, so as to reduce the number of samples in the solution set. Then, a joint observation probability measure based on laser error is proposed to measure the quality of solutions. Finally, using the clustering results and the measurement results of observation probability, an effective solution selection strategy based on the estimation of the effective lower bound of observation probability is proposed, which transforms the optimized initial value from the optimal solution to the candidate set of effective solutions, and improves the accuracy of calibration results. Comparing with the existing methods, results of both simulation and real data experiment show that the proposed algorithm improves significantly the true solution hit rate by 16%～20% under different number of checkerboards and 6%～20% under different noise levels.
Considering the multi solution problem of minimum solution method for calibration of 2D lidar and camera, a calibration method based on the estimation of the effective lower bound of observation probability is proposed. Firstly, a hierarchical clustering method with minimum solution set is proposed which should be used to replace the original solution set with each kind of optimal solution, so as to reduce the number of samples in the solution set. Then, a joint observation probability measure based on laser error is proposed to measure the quality of solutions. Finally, using the clustering results and the measurement results of observation probability, an effective solution selection strategy based on the estimation of the effective lower bound of observation probability is proposed, which transforms the optimized initial value from the optimal solution to the candidate set of effective solutions, and improves the accuracy of calibration results. Comparing with the existing methods, results of both simulation and real data experiment show that the proposed algorithm improves significantly the true solution hit rate by 16%～20% under different number of checkerboards and 6%～20% under different noise levels.
2022, 44(7): 2488-2495.
doi: 10.11999/JEIT210439
Abstract:
Considering poor performance of target state estimation for Gaussian Mixture Probability Hypothesis Density(GM-PHD) filter when the target velocity is unknown or inaccurate, a combined smoothing filtering algorithm for motion parameter estimation based on GM-PHD is proposed. The velocity information is extracted from the target state, and the accuracy of velocity estimation is improved through the combined processing of median smoothing and linear smoothing. Then, the velocity is fed back to the state transition equation of the GM-PHD filter to improve the accuracy of state prediction. Simulation results show that the proposed algorithm can significantly improve the state estimation performance of GM-PHD filter when the target velocity is unknown or inaccurate.
Considering poor performance of target state estimation for Gaussian Mixture Probability Hypothesis Density(GM-PHD) filter when the target velocity is unknown or inaccurate, a combined smoothing filtering algorithm for motion parameter estimation based on GM-PHD is proposed. The velocity information is extracted from the target state, and the accuracy of velocity estimation is improved through the combined processing of median smoothing and linear smoothing. Then, the velocity is fed back to the state transition equation of the GM-PHD filter to improve the accuracy of state prediction. Simulation results show that the proposed algorithm can significantly improve the state estimation performance of GM-PHD filter when the target velocity is unknown or inaccurate.
2022, 44(7): 2496-2503.
doi: 10.11999/JEIT210341
Abstract:
This paper studies the task of crowd anomaly detection. Considering the problems of crowd scene video background being redundant, susceptible to light and noise, and actual deployment, a Crowd anomaly Multi-scale feature memory network (CaMsm-net) is proposed. In order to distinguish the occurrence of anomalies from multiple angles and integrate better various types of information, a dual-branch shared unit structure is adopted by the network, the original frame and the background modeled frame are simultaneously input into the network structure. To predict the two branches separately, the prediction error was used to determine the abnormality, and from the perspective of practical application, the depthwise separable convolutionand data augmentation methods are added to the framework to ensure the accuracy of detection and the feasibility of deployment. Experiments on the public University of MinNesota (UMN) population dataset and the actual monitoring of the train station exit dataset show that the Area Under Curve (AUC) indicators reach 99.2% and 84.1% respectively, and the average detection accuracy rates are 95.9% and 81.7%, which proves the proposed algorithm can better detect the occurrence of various crowd abnormalities and has wider applicability.
This paper studies the task of crowd anomaly detection. Considering the problems of crowd scene video background being redundant, susceptible to light and noise, and actual deployment, a Crowd anomaly Multi-scale feature memory network (CaMsm-net) is proposed. In order to distinguish the occurrence of anomalies from multiple angles and integrate better various types of information, a dual-branch shared unit structure is adopted by the network, the original frame and the background modeled frame are simultaneously input into the network structure. To predict the two branches separately, the prediction error was used to determine the abnormality, and from the perspective of practical application, the depthwise separable convolutionand data augmentation methods are added to the framework to ensure the accuracy of detection and the feasibility of deployment. Experiments on the public University of MinNesota (UMN) population dataset and the actual monitoring of the train station exit dataset show that the Area Under Curve (AUC) indicators reach 99.2% and 84.1% respectively, and the average detection accuracy rates are 95.9% and 81.7%, which proves the proposed algorithm can better detect the occurrence of various crowd abnormalities and has wider applicability.
2022, 44(7): 2504-2511.
doi: 10.11999/JEIT210400
Abstract:
In order to solve the problem of poor quality and slow speed in turbid water image enhancement based on Cycle Generative Adversarial Networks (CycleGAN), a scalable, selective and efficient block Bottleneck Selective Dilated Kernel (BSDK) is proposed, and a new generator network BSDKNet is redesigned by stacking BSDK. At the same time, Multi-scale Loss Function (MLF) is proposed to improve the structural similarity of the clear water image and the generated clear water image. On our turbid water image enhancement dataset Turbid and Clear (TC), the classification accuracy of the proposed BM-CycleGAN is 3.27% higher than that of classical CycleGAN. The generator parameters of BM-CycleGAN is 4.15 MB lower than that of CycleGAN, and the time consuming of BM-CycleGAN is 0.107 s less than that of CycleGAN. The experimental results show that BM-CycleGAN is suitable for turbid water image enhancement.
In order to solve the problem of poor quality and slow speed in turbid water image enhancement based on Cycle Generative Adversarial Networks (CycleGAN), a scalable, selective and efficient block Bottleneck Selective Dilated Kernel (BSDK) is proposed, and a new generator network BSDKNet is redesigned by stacking BSDK. At the same time, Multi-scale Loss Function (MLF) is proposed to improve the structural similarity of the clear water image and the generated clear water image. On our turbid water image enhancement dataset Turbid and Clear (TC), the classification accuracy of the proposed BM-CycleGAN is 3.27% higher than that of classical CycleGAN. The generator parameters of BM-CycleGAN is 4.15 MB lower than that of CycleGAN, and the time consuming of BM-CycleGAN is 0.107 s less than that of CycleGAN. The experimental results show that BM-CycleGAN is suitable for turbid water image enhancement.
2022, 44(7): 2512-2521.
doi: 10.11999/JEIT210818
Abstract:
According to the mechanism of visual information interactive perception between dual Visual Pathways(VP) in the biological vision system, a new method of contour detection is proposed. Considering the visual stimulus in the hypocritical pathway flowing through multi-level and different-scale receptive fields, a multi-scale contour fusion contour perception model is proposed. Based on the contrast adaptation mechanism and directional sensitivity of the visual pathway on the cortex, salient visual features are extracted. The interactive perception mechanism of the dual vision pathway is simulated, a pulse coding model is constructed guided by the information flow interaction in the V1 cortex, to extract the saliency contour. An inhibition model of feature modulation non-classical receptive field is proposed in the Superior Colliculus(SC) shallow layer, to achieve texture inhibition. Finally, the contour response results in the dual-view path is modified and fused to obtain the final contour response. For the test of the RUG40 image library, the optimal average P index of the whole dataset and each graph is 0.51 and 0.57 respectively. For the test of the BSDS500 image library, the Optimal Scale (ODS) of the Dataset is 0.68. The results show that the method in this paper can effectively highlight the outline of the subject and suppress the textured background, which provides a new idea for the subsequent image understanding and analysis based on the visual mechanism.
According to the mechanism of visual information interactive perception between dual Visual Pathways(VP) in the biological vision system, a new method of contour detection is proposed. Considering the visual stimulus in the hypocritical pathway flowing through multi-level and different-scale receptive fields, a multi-scale contour fusion contour perception model is proposed. Based on the contrast adaptation mechanism and directional sensitivity of the visual pathway on the cortex, salient visual features are extracted. The interactive perception mechanism of the dual vision pathway is simulated, a pulse coding model is constructed guided by the information flow interaction in the V1 cortex, to extract the saliency contour. An inhibition model of feature modulation non-classical receptive field is proposed in the Superior Colliculus(SC) shallow layer, to achieve texture inhibition. Finally, the contour response results in the dual-view path is modified and fused to obtain the final contour response. For the test of the RUG40 image library, the optimal average P index of the whole dataset and each graph is 0.51 and 0.57 respectively. For the test of the BSDS500 image library, the Optimal Scale (ODS) of the Dataset is 0.68. The results show that the method in this paper can effectively highlight the outline of the subject and suppress the textured background, which provides a new idea for the subsequent image understanding and analysis based on the visual mechanism.
2022, 44(7): 2522-2530.
doi: 10.11999/JEIT210184
Abstract:
In view of the non-convex defect of the image selective segmentation model based on geodesic distance, a convex selective segmentation model by convex relaxation is proposed. The relationship between the solution of the convex model and that of the original model is given. Then by incorporating the Alternating Direction Method of Multipliers (ADMM), this paper designs a fast algorithm for numerically solving the convex model and gives the convergence of the algorithm. Finally, numerical experimental results show that convergence speed of the proposed algorithm is much better than the original algorithm based on the additive operator splitting method, and the segmentation results of the proposed algorithm are more accurate.
In view of the non-convex defect of the image selective segmentation model based on geodesic distance, a convex selective segmentation model by convex relaxation is proposed. The relationship between the solution of the convex model and that of the original model is given. Then by incorporating the Alternating Direction Method of Multipliers (ADMM), this paper designs a fast algorithm for numerically solving the convex model and gives the convergence of the algorithm. Finally, numerical experimental results show that convergence speed of the proposed algorithm is much better than the original algorithm based on the additive operator splitting method, and the segmentation results of the proposed algorithm are more accurate.
2022, 44(7): 2531-2538.
doi: 10.11999/JEIT210229
Abstract:
In the field of speech separation using deep learning, the Recurrent Neural Network (RNN) is commonly used for speech separation, but the network model has a gradient descent problem in the separation process, and the separation result is not ideal. Considering this problem, this paper uses Long Short-Term Memory (LSTM) network to explore the signal separation, which makes up for the deficiency of RNN network. The separation of multi-channel vocal signals is more complicated, and most of the separation methods used at this stage are based on the spectrum mapping method, and the spatial information of the voice signal is not effectively used. In response to this problem, this paper combines the beamforming algorithm and the LSTM network to propose a beamforming LSTM algorithm. The voice files of three speakers are randomly selected from the TIMIT voice library, and the super-pointing beamforming algorithm is used to obtain beams in three different directions. The spectral amplitude characteristics in each beam are extracted, and a neural network is constructed to predict the masking value. The to-be-separated speech signal spectrum is obtained. and the time-domain signal is constructed, and the speech separation is realized. The algorithm makes full use of the spatial characteristics of the speech signal and the signal frequency domain characteristics. The effect of speech separation in different directions is verified through experiments. Compared with the IBM-LSTM network, at 60-degree direction, this algorithm improves Perceptual Evaluation of Speech Quality (PESQ) by 0.59, Short-Time Objective Intelligibility (STOI) index by 0.06, and Signal to Noise Ratio (SNR) by 1.13 dB. At the other two reverse directions, the experimental results also prove that the algorithm has better separation performance than the IBM-LSTM algorithm and the RNN algorithm.
In the field of speech separation using deep learning, the Recurrent Neural Network (RNN) is commonly used for speech separation, but the network model has a gradient descent problem in the separation process, and the separation result is not ideal. Considering this problem, this paper uses Long Short-Term Memory (LSTM) network to explore the signal separation, which makes up for the deficiency of RNN network. The separation of multi-channel vocal signals is more complicated, and most of the separation methods used at this stage are based on the spectrum mapping method, and the spatial information of the voice signal is not effectively used. In response to this problem, this paper combines the beamforming algorithm and the LSTM network to propose a beamforming LSTM algorithm. The voice files of three speakers are randomly selected from the TIMIT voice library, and the super-pointing beamforming algorithm is used to obtain beams in three different directions. The spectral amplitude characteristics in each beam are extracted, and a neural network is constructed to predict the masking value. The to-be-separated speech signal spectrum is obtained. and the time-domain signal is constructed, and the speech separation is realized. The algorithm makes full use of the spatial characteristics of the speech signal and the signal frequency domain characteristics. The effect of speech separation in different directions is verified through experiments. Compared with the IBM-LSTM network, at 60-degree direction, this algorithm improves Perceptual Evaluation of Speech Quality (PESQ) by 0.59, Short-Time Objective Intelligibility (STOI) index by 0.06, and Signal to Noise Ratio (SNR) by 1.13 dB. At the other two reverse directions, the experimental results also prove that the algorithm has better separation performance than the IBM-LSTM algorithm and the RNN algorithm.
2022, 44(7): 2539-2546.
doi: 10.11999/JEIT210160
Abstract:
Implementation of fast, accurate and adaptable reconstruction is one of the core topics in Tunable Diode Laser Absorption Tomography (TDLAT). In existing algorithms, a certain region at the center of combustion field is usually set as the Region of Interest (RoI). Temperature image of RoI is reconstructed from the absorbance for laser beams passing through the whole tomographic field. It will cause deviations in the reconstructed image. To address this issue, a spatial hierarchical discretization and a Hierarchical Temperature Tomography scheme based on Residual Network (HTT-ResNet) are proposed for TDLAT. It reconstructs the temperature image of the entire combustion field from limited amount of absorbance measurements, and configures optimally computational resources and imaging resolution to describe the temperature distribution in RoI with better spatial resolution. Experiments using random multimodal Gaussian flame models and the measured data of the actual TDLAT system both show that temperature images reconstructed by HTT-ResNet can accurately locate the flame and clearly describe the temperature profile in the combustion field.
Implementation of fast, accurate and adaptable reconstruction is one of the core topics in Tunable Diode Laser Absorption Tomography (TDLAT). In existing algorithms, a certain region at the center of combustion field is usually set as the Region of Interest (RoI). Temperature image of RoI is reconstructed from the absorbance for laser beams passing through the whole tomographic field. It will cause deviations in the reconstructed image. To address this issue, a spatial hierarchical discretization and a Hierarchical Temperature Tomography scheme based on Residual Network (HTT-ResNet) are proposed for TDLAT. It reconstructs the temperature image of the entire combustion field from limited amount of absorbance measurements, and configures optimally computational resources and imaging resolution to describe the temperature distribution in RoI with better spatial resolution. Experiments using random multimodal Gaussian flame models and the measured data of the actual TDLAT system both show that temperature images reconstructed by HTT-ResNet can accurately locate the flame and clearly describe the temperature profile in the combustion field.
2022, 44(7): 2547-2558.
doi: 10.11999/JEIT210331
Abstract:
The denoising of ultrasound images is very important to improve the visual quality of ultrasound images and to accomplish other related computer vision tasks. The feature information in ultrasound images is similar to the speckle noise signal. The existing denoising methods for ultrasound images denoising are easy to cause the loss of texture features of ultrasound images, which will cause serious interference to the accuracy of clinical diagnosis. Therefore, in the process of speckle noise removal, the edge texture information of images should be retained as far as possible to complete better the task of ultrasound images denoising. RED-SENet (Residual Encoder-Decoder with Squeeze-and-Excitation Network), a channel adaptive denoising model based on residual encoder-decoder is presented, which can effectively remove speckle noise in ultrasound images. By introducing the attention deconvolution residual block in the decoder part of the denoising model, the model can learn and use the global information, selective emphasizing the content features of the key channels and suppress the useless features, which can improve the denoising performance of the model. The model is qualitatively evaluated and quantitatively analyzed on 2 private datasets and 2 public datasets, respectively. Compared with some advanced methods, the denoising performance of the model is significantly improved, and it has advantages in noise suppression and structure preservation.
The denoising of ultrasound images is very important to improve the visual quality of ultrasound images and to accomplish other related computer vision tasks. The feature information in ultrasound images is similar to the speckle noise signal. The existing denoising methods for ultrasound images denoising are easy to cause the loss of texture features of ultrasound images, which will cause serious interference to the accuracy of clinical diagnosis. Therefore, in the process of speckle noise removal, the edge texture information of images should be retained as far as possible to complete better the task of ultrasound images denoising. RED-SENet (Residual Encoder-Decoder with Squeeze-and-Excitation Network), a channel adaptive denoising model based on residual encoder-decoder is presented, which can effectively remove speckle noise in ultrasound images. By introducing the attention deconvolution residual block in the decoder part of the denoising model, the model can learn and use the global information, selective emphasizing the content features of the key channels and suppress the useless features, which can improve the denoising performance of the model. The model is qualitatively evaluated and quantitatively analyzed on 2 private datasets and 2 public datasets, respectively. Compared with some advanced methods, the denoising performance of the model is significantly improved, and it has advantages in noise suppression and structure preservation.
2022, 44(7): 2559-2567.
doi: 10.11999/JEIT210424
Abstract:
In the application of reservoir geological description using logging data, some of the logging curves are often distorted or missing, and for this reason, the recovery of logging curves is a research hotspot and difficulty in related research fields. Traditional signal recovery methods and recovery methods based on machine learning such as neural networks do not adequately represent and utilize correlation information between different logging curves of the same well, and have poor adaptability to cross-well models. In response to these problems, a log curve restoration method is proposed in this paper based on Long Short-Term Memory (LSTM) neural network multi-scale symbiosis mining: on the basis of neural network log curve restoration method, by introducing the multi-scale Gray Level Co-occurrence Matrix (GLCM) completes the characterization of the lateral correlation information between different logging curves so as to realize the full utilization of the vertical and horizontal semantic information of the logging curve set, thereby realizing the restoration of missing logging curves. The experimental results show that, compared with the BP neural network, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Deep Forest (DF) and LSTM network methods, the method proposed in this paper has better signal restoration accuracy, and the constructed model has a certain cross-well adaptability.
In the application of reservoir geological description using logging data, some of the logging curves are often distorted or missing, and for this reason, the recovery of logging curves is a research hotspot and difficulty in related research fields. Traditional signal recovery methods and recovery methods based on machine learning such as neural networks do not adequately represent and utilize correlation information between different logging curves of the same well, and have poor adaptability to cross-well models. In response to these problems, a log curve restoration method is proposed in this paper based on Long Short-Term Memory (LSTM) neural network multi-scale symbiosis mining: on the basis of neural network log curve restoration method, by introducing the multi-scale Gray Level Co-occurrence Matrix (GLCM) completes the characterization of the lateral correlation information between different logging curves so as to realize the full utilization of the vertical and horizontal semantic information of the logging curve set, thereby realizing the restoration of missing logging curves. The experimental results show that, compared with the BP neural network, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Deep Forest (DF) and LSTM network methods, the method proposed in this paper has better signal restoration accuracy, and the constructed model has a certain cross-well adaptability.
2022, 44(7): 2568-2575.
doi: 10.11999/JEIT210412
Abstract:
Source code vulnerability detection is an important method to ensure the security of software system. In recent years, a variety of deep learning models are applied to source code vulnerability detection, which improves greatly the efficiency of vulnerability detection. However, there are still some problems in source code vulnerability detection based on deep learning, such as too many words outside the database caused by user-defined identifier, inaccurate semantics of embedded word vector, lack of interpretability of neural network model, and so on. A new software source code vulnerability detection method is proposed based on Convolution Neural Networks (CNN) and Global Average Pooling (GAP) interpretability model. Firstly, some user-defined identifiers are normalized in the source code preprocessing, and one hot coding is used for word embedding to alleviate the problem of too many words outside the database. Then, CNN-GAP neural network model is built to identify the functions containing CWE-119 type vulnerabilities. Finally, Class Activation Mapping (CAM) interpretable method is used to output visually the results and identify the codes that may be related to vulnerabilities. Compared with the model proposed by Russell and Vuldeepecker model proposed by Li et al., the experimental results show that CNN-GAP model can achieve quite or even better performance, and has a certain interpretability, which is convenient for researchers to analyze the vulnerability more deeply.
Source code vulnerability detection is an important method to ensure the security of software system. In recent years, a variety of deep learning models are applied to source code vulnerability detection, which improves greatly the efficiency of vulnerability detection. However, there are still some problems in source code vulnerability detection based on deep learning, such as too many words outside the database caused by user-defined identifier, inaccurate semantics of embedded word vector, lack of interpretability of neural network model, and so on. A new software source code vulnerability detection method is proposed based on Convolution Neural Networks (CNN) and Global Average Pooling (GAP) interpretability model. Firstly, some user-defined identifiers are normalized in the source code preprocessing, and one hot coding is used for word embedding to alleviate the problem of too many words outside the database. Then, CNN-GAP neural network model is built to identify the functions containing CWE-119 type vulnerabilities. Finally, Class Activation Mapping (CAM) interpretable method is used to output visually the results and identify the codes that may be related to vulnerabilities. Compared with the model proposed by Russell and Vuldeepecker model proposed by Li et al., the experimental results show that CNN-GAP model can achieve quite or even better performance, and has a certain interpretability, which is convenient for researchers to analyze the vulnerability more deeply.
2022, 44(7): 2576-2583.
doi: 10.11999/JEIT210448
Abstract:
Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Existing papers do not pay attention to the relationship between adversarial attacks and statistical diagnosis, a classical branch of statistics. In this paper, the consistency of the two theories is analyzed, and the local influence analysis model, an important achievement of statistical diagnosis, is introduced into adversarial attack on GNNs. Firstly, the local influence analysis model is established to derive the equation of perturbation selecting of attacks, and the physical meaning of this equation is a measurement of the influence of perturbation on model training parameters. Secondly, to reduce the computational complexity, according to the physical meaning of the perturbation selecting equation, the approximate equation is obtained. Finally, the projected gradient descent algorithm is introduced to implement disturbance selecting. Experimental results show that it is reasonable to introduce the local influence analysis model into the field of adversarial attacks on graph neural network; Compared with the existing attack methods, the proposed method is more effective.
Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Existing papers do not pay attention to the relationship between adversarial attacks and statistical diagnosis, a classical branch of statistics. In this paper, the consistency of the two theories is analyzed, and the local influence analysis model, an important achievement of statistical diagnosis, is introduced into adversarial attack on GNNs. Firstly, the local influence analysis model is established to derive the equation of perturbation selecting of attacks, and the physical meaning of this equation is a measurement of the influence of perturbation on model training parameters. Secondly, to reduce the computational complexity, according to the physical meaning of the perturbation selecting equation, the approximate equation is obtained. Finally, the projected gradient descent algorithm is introduced to implement disturbance selecting. Experimental results show that it is reasonable to introduce the local influence analysis model into the field of adversarial attacks on graph neural network; Compared with the existing attack methods, the proposed method is more effective.
2022, 44(7): 2584-2591.
doi: 10.11999/JEIT210300
Abstract:
Certificateless proxy signcryption plays an increasingly significant role in information security fields. Most of certificateless proxy signcryption schemes are based on traditional mathematic theory and can not resist the quantum computing attacks. In this paper, a new CertificateLess Proxy SignCryption from Lattice (L-CLPSC) is proposed by using lattice-based cryptography technology. L-CLPSC is indistinguishable against adaptive chosen-ciphertext attacks and unforgeable against adaptive chosen-message attacks under Learning With Errors (LWE) and Small Integer Solution (SIS) assumptions. Comparison shows L-CLPSC has higher computation efficiency and lower communication overhead.
Certificateless proxy signcryption plays an increasingly significant role in information security fields. Most of certificateless proxy signcryption schemes are based on traditional mathematic theory and can not resist the quantum computing attacks. In this paper, a new CertificateLess Proxy SignCryption from Lattice (L-CLPSC) is proposed by using lattice-based cryptography technology. L-CLPSC is indistinguishable against adaptive chosen-ciphertext attacks and unforgeable against adaptive chosen-message attacks under Learning With Errors (LWE) and Small Integer Solution (SIS) assumptions. Comparison shows L-CLPSC has higher computation efficiency and lower communication overhead.
2022, 44(7): 2592-2601.
doi: 10.11999/JEIT210347
Abstract:
Quasi-optical mode converter is an important component to realize the high-efficiency output for a high-power gyrotron oscillator. In this paper, design and experiments of a quasi-optical mode converter, consisting of a Denisov-type launcher and three quasi-optical mirrors, are carried out for the development of 140 GHz/TE28,8 mode gyrotron oscillator. Based on scalar diffraction method, the field distribution at the radiation aperture of the Denisov-type launcher is optimized to make the vector correlation of the aperture field with the ideal Gaussian field reach 96.2%. Based on the geometric optics method and the Gaussian beam matching method, the focusing mirror and the beam shaping mirrors are designed. A 3D full-wave analysis software Surf3D is used to obtain the field distribution on each mirror surface and the output window, which shows that the output beam with Gaussian mode content of 96.67% is obtained at the output window. The total power conversion efficiency of the quasi-optical mode converter is 93.98%. The design and experiments of the quasi-optical mode converter are performed to use the output signal of the self-developed TE28,8 mode generator as its input one under the conditions of the simulated demonstration for its conversion characteristics and the strict control for machining precision as well as the process of the converter assemble and testing. The tested results indicate that the design is in good agreement with the experiment, which is helpful for engineering design and experimental demonstration of the quasi-optical mode converter development.
Quasi-optical mode converter is an important component to realize the high-efficiency output for a high-power gyrotron oscillator. In this paper, design and experiments of a quasi-optical mode converter, consisting of a Denisov-type launcher and three quasi-optical mirrors, are carried out for the development of 140 GHz/TE28,8 mode gyrotron oscillator. Based on scalar diffraction method, the field distribution at the radiation aperture of the Denisov-type launcher is optimized to make the vector correlation of the aperture field with the ideal Gaussian field reach 96.2%. Based on the geometric optics method and the Gaussian beam matching method, the focusing mirror and the beam shaping mirrors are designed. A 3D full-wave analysis software Surf3D is used to obtain the field distribution on each mirror surface and the output window, which shows that the output beam with Gaussian mode content of 96.67% is obtained at the output window. The total power conversion efficiency of the quasi-optical mode converter is 93.98%. The design and experiments of the quasi-optical mode converter are performed to use the output signal of the self-developed TE28,8 mode generator as its input one under the conditions of the simulated demonstration for its conversion characteristics and the strict control for machining precision as well as the process of the converter assemble and testing. The tested results indicate that the design is in good agreement with the experiment, which is helpful for engineering design and experimental demonstration of the quasi-optical mode converter development.
2022, 44(7): 2602-2610.
doi: 10.11999/JEIT210337
Abstract:
In order to explore the bursting oscillations and its mechanism of the piecewise linear memristor-based system, a 4D piecewise linear memristor-based system with two-timescale is created by introducing a piecewise linear memristive model and a periodically excitation based on a non-autonomous system. Due to the introduction of the piecewise linear memristive model, the system is divided into different subsystems by non-smooth boundaries. It is pointed that not only the stability of the nominal equilibrium orbits but also the non-smooth boundaries may influence the bursting, resulting in the abrupt jumping of the trajectory and non-smooth bifurcations, which reveal two different bursting patterns. Based on the generalized differential of Clarke, the bifurcation mechanism is studied. And the bursting mechanism is analyzed by time history and transformation phase diagram. Finally, the numerical simulation and Multisim circuit simulation are conducted to verify the validity. This paper is of great significance to study the dynamical behavior of the piecewise linear memristor and its application to practice.
In order to explore the bursting oscillations and its mechanism of the piecewise linear memristor-based system, a 4D piecewise linear memristor-based system with two-timescale is created by introducing a piecewise linear memristive model and a periodically excitation based on a non-autonomous system. Due to the introduction of the piecewise linear memristive model, the system is divided into different subsystems by non-smooth boundaries. It is pointed that not only the stability of the nominal equilibrium orbits but also the non-smooth boundaries may influence the bursting, resulting in the abrupt jumping of the trajectory and non-smooth bifurcations, which reveal two different bursting patterns. Based on the generalized differential of Clarke, the bifurcation mechanism is studied. And the bursting mechanism is analyzed by time history and transformation phase diagram. Finally, the numerical simulation and Multisim circuit simulation are conducted to verify the validity. This paper is of great significance to study the dynamical behavior of the piecewise linear memristor and its application to practice.
