Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna apertu...Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.展开更多
Although federated learning(FL)has become very popular recently,it is vulnerable to gradient leakage attacks.Recent studies have shown that attackers can reconstruct clients’private data from shared models or gradien...Although federated learning(FL)has become very popular recently,it is vulnerable to gradient leakage attacks.Recent studies have shown that attackers can reconstruct clients’private data from shared models or gradients.Many existing works focus on adding privacy protection mechanisms to prevent user privacy leakages,such as differential privacy(DP)and homomorphic encryption.These defenses may cause an increase in computation and communication costs or degrade the performance of FL.Besides,they do not consider the impact of wireless network resources on the FL training process.Herein,we propose weight compression,a defense method to prevent gradient leakage attacks for FL over wireless networks.The gradient compression matrix is determined by the user’s location and channel conditions.We also add Gaussian noise to the compressed gradients to strengthen the defense.This joint learning of wireless resource allocation and weight compression matrix is formulated as an optimization problem with the objective of minimizing the FL loss function.To find the solution,we first analyze the convergence rate of FL and quantify the effect of the weight matrix on FL convergence.Then,we seek the optimal resource block(RB)allocation by exhaustive search or ant colony optimization(ACO)and then use the CVX toolbox to obtain the optimal weight matrix to minimize the optimization function.The simulation results show that the optimized RB can accelerate the convergence of FL.展开更多
Reconfigurable intelligent surface(RIS)as a promising technology has been proposed to change weak communication environ-ments.However,most of the current resource allocation(RA)schemes have focused on RIS-assisted hom...Reconfigurable intelligent surface(RIS)as a promising technology has been proposed to change weak communication environ-ments.However,most of the current resource allocation(RA)schemes have focused on RIS-assisted homogeneous networks,and there is still no open works about RA schemes of RIS-assisted heterogeneous networks(HetNets).In this paper,we design an RA scheme for a RIS-assisted HetNet with non-orthogonal multiple access to improve spectrum efficiency and transmission rates.In particular,we jointly optimize the transmit power of the small-cell base station and the phase-shift matrix of the RIS to maximize the sum rates of all small-cell users,subject to the unit modulus constraint,the minimum signal-to-interference-plus-noise ratio constraint,and the cross-tier interference constraint for protecting communication quality of microcell users.An efficient suboptimal RA scheme is proposed based on the alternating iteration ap-proach,and successive convex approximation and logarithmic transformation approach.Simulation results verify the effectiveness of the pro-posed scheme in terms of data rates.展开更多
文摘Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.
文摘Although federated learning(FL)has become very popular recently,it is vulnerable to gradient leakage attacks.Recent studies have shown that attackers can reconstruct clients’private data from shared models or gradients.Many existing works focus on adding privacy protection mechanisms to prevent user privacy leakages,such as differential privacy(DP)and homomorphic encryption.These defenses may cause an increase in computation and communication costs or degrade the performance of FL.Besides,they do not consider the impact of wireless network resources on the FL training process.Herein,we propose weight compression,a defense method to prevent gradient leakage attacks for FL over wireless networks.The gradient compression matrix is determined by the user’s location and channel conditions.We also add Gaussian noise to the compressed gradients to strengthen the defense.This joint learning of wireless resource allocation and weight compression matrix is formulated as an optimization problem with the objective of minimizing the FL loss function.To find the solution,we first analyze the convergence rate of FL and quantify the effect of the weight matrix on FL convergence.Then,we seek the optimal resource block(RB)allocation by exhaustive search or ant colony optimization(ACO)and then use the CVX toolbox to obtain the optimal weight matrix to minimize the optimization function.The simulation results show that the optimized RB can accelerate the convergence of FL.
基金partially supported by the China National Key R&D Program under Grant No. 2021YFA1000502National Natural Science Foundation of China under Grant No. 62101492+4 种基金Zhejiang Provincial Natural Science Foundation of China under Grant No. LR22F010002Distinguished Young Scholars of the National Natural Science Foundation of ChinaNg Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA GrantZhejiang University Education Foundation Qizhen Scholar FoundationFundamental Research Funds for the Central Universities under Grant No. 2021FZZX001-21
文摘Reconfigurable intelligent surface(RIS)as a promising technology has been proposed to change weak communication environ-ments.However,most of the current resource allocation(RA)schemes have focused on RIS-assisted homogeneous networks,and there is still no open works about RA schemes of RIS-assisted heterogeneous networks(HetNets).In this paper,we design an RA scheme for a RIS-assisted HetNet with non-orthogonal multiple access to improve spectrum efficiency and transmission rates.In particular,we jointly optimize the transmit power of the small-cell base station and the phase-shift matrix of the RIS to maximize the sum rates of all small-cell users,subject to the unit modulus constraint,the minimum signal-to-interference-plus-noise ratio constraint,and the cross-tier interference constraint for protecting communication quality of microcell users.An efficient suboptimal RA scheme is proposed based on the alternating iteration ap-proach,and successive convex approximation and logarithmic transformation approach.Simulation results verify the effectiveness of the pro-posed scheme in terms of data rates.
