This paper presents a circularly polarized(CP)multiple-input multiple-output(MIMO)antenna with wideband and compact size characteristics.The proposed MIMO antenna consists of two wideband dual-CP radiating elements,wh...This paper presents a circularly polarized(CP)multiple-input multiple-output(MIMO)antenna with wideband and compact size characteristics.The proposed MIMO antenna consists of two wideband dual-CP radiating elements,which are positioned in proximity and decoupled by shorting vias.Accordingly,only two radiating elements are required for a 4-port MIMO array.It distinguishes the proposed design approach from the others,in which 4-port MIMO antennas commonly need four radiating elements.The measured results confirm the wideband performance of the proposed antenna with 10 dB isolation operating bandwidth of 15%(4.68–5.44 GHz),and 15 dB isolation operating bandwidth of 8.2%(4.68–5.08 GHz).Besides,4-port MIMO antenna can be realized with compact dimensions of 0.84λ×0.46λ×0.05λat 4.68 GHz.In comparison with the related CP MIMO antennas,the proposed antenna can work with a higher number of operating ports while achieving smaller overall dimensions.Besides,large operating bandwidth is also another advantage of the proposed work compared to the others.展开更多
In massive multiple-input multiple-output(MIMO)systems utilizing frequency division duplexing,optimizing system performance requires user equipment(UE)to compress downlink channel state information(CSI)and transmit it...In massive multiple-input multiple-output(MIMO)systems utilizing frequency division duplexing,optimizing system performance requires user equipment(UE)to compress downlink channel state information(CSI)and transmit it to the base station(BS).As the number of antennas increases,there is a significant rise in the overhead related to CSI feedback,posing considerable challenges to the precise acquisition of CSI by the BS.Existing approaches to CSI feedback utilizing deep learning techniques face challenges such as significant feedback overhead and limited precision in the reconstruction process.This study presents a novel lightweight CSI feedback framework known as the dual attention neural network(DANet).Within the DANet architecture,a dual attention module(DAM)is designed to enhance the network's performance.This DAM includes both channel attention blocks and spatial attention blocks.The channel attention blocks direct the model's focus toward channel features rich in information content while simultaneously suppressing less significant features.This approach enables the extraction of temporal correlations within the CSI matrix.The spatial attention block aids in extracting the correlation between the delay domain and the angle domain in the CSI matrix.By enhancing neural network performance,the DAM reduces information dispersion while enhancing the representation of global interactions.Simulation results demonstrate that DANet exhibits superior normalized mean square error and cosine similarity with comparable complexity compared to existing advanced CSI feedback methods.展开更多
To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especial...To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especially in the frequency division duplex(FDD)systems.However,due to the enormous number of antennas in massive MIMO systems,the feedback overhead of downlink CSI acquisition is extremely large.To address this issue,deep learning(DL)techniques have been introduced to de velop high-accuracy feedback strategies under limited backhaul constraints.In this paper,we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems.Specifically,we introduce the conventional CSI compression and feedback schemes and the existing problems.Besides,we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques.We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback.In addition,we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.展开更多
Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of ...Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of managing finite transmit and receive antennas and transmit power systematically to enhance detection performance.To tackle the multidimensional resource optimization challenge,we introduce a Cooperative Transmit-Receive Antenna Selection and Power Allocation(CTRSPA)strategy.It employs a perception-action cycle that incorporates uncertain external support information to optimize worst-case detection performance with multiple targets.First,we derive a closed-form expression that incorporates uncertainty for the noncoherent integration squared-law detection probability using the Neyman-Pearson criterion.Subsequently,a joint optimization model for antenna selection and power allocation in CFAR detection is formulated,incorporating practical radar resource constraints.Mathematically,this represents an NPhard problem involving coupled continuous and Boolean variables.We propose a three-stage method—Reformulation,Node Picker,and Convex Power Allocation—that capitalizes on the independent convexity of the optimization model for each variable,ensuring a near-optimal result.Simulations confirm the approach's effectiveness,efficiency,and timeliness,particularly for large-scale radar networks,and reveal the impact of threat levels,system layout,and detection parameters on resource allocation.展开更多
Multiple-input multiple-output(MIMO)systems are essential for improving capacity and reliability in semantic communications.Existing methods mainly design the channel-aware neural networks but neglect the underlying s...Multiple-input multiple-output(MIMO)systems are essential for improving capacity and reliability in semantic communications.Existing methods mainly design the channel-aware neural networks but neglect the underlying signal distribution.In this paper,we develop a denoising diffusion null-space model-based module over MIMO channels(DDNM-MIMO),which is a plug-in module deployed at the receiver.By modeling the MIMO channel,precoding,and equalization as a linear transformation with additive noise,we design corresponding linear and scaling matrices to construct a sampling process for denoising the received signal.The DDNM-MIMO integrates channel state information(CSI)embedding,supporting both closed-loop MIMO with CSI at the transmitter and open-loop MIMO with CSI at the receiver,thereby improving channel adaptability across various noise levels.As a plug-in,the DDNM-MIMO module operates independently of the joint source-channel coding(JSCC)coder structure,offering flexible integration into diverse systems.Experimental results show that DDNM-MIMO effectively reduces the mean square errors(MSE)between the encoded and equalized signals.Consequently,the proposed DDNM-MIMO semantic communication system achieves superior image reconstruction performance compared to existing JSCC-based semantic communication method.展开更多
摘要This paper presents a circularly polarized(CP)multiple-input multiple-output(MIMO)antenna with wideband and compact size characteristics.The proposed MIMO antenna consists of two wideband dual-CP radiating elements,which are positioned in proximity and decoupled by shorting vias.Accordingly,only two radiating elements are required for a 4-port MIMO array.It distinguishes the proposed design approach from the others,in which 4-port MIMO antennas commonly need four radiating elements.The measured results confirm the wideband performance of the proposed antenna with 10 dB isolation operating bandwidth of 15%(4.68–5.44 GHz),and 15 dB isolation operating bandwidth of 8.2%(4.68–5.08 GHz).Besides,4-port MIMO antenna can be realized with compact dimensions of 0.84λ×0.46λ×0.05λat 4.68 GHz.In comparison with the related CP MIMO antennas,the proposed antenna can work with a higher number of operating ports while achieving smaller overall dimensions.Besides,large operating bandwidth is also another advantage of the proposed work compared to the others.
基金National Natural Science Foundation of China(12005108)。
摘要In massive multiple-input multiple-output(MIMO)systems utilizing frequency division duplexing,optimizing system performance requires user equipment(UE)to compress downlink channel state information(CSI)and transmit it to the base station(BS).As the number of antennas increases,there is a significant rise in the overhead related to CSI feedback,posing considerable challenges to the precise acquisition of CSI by the BS.Existing approaches to CSI feedback utilizing deep learning techniques face challenges such as significant feedback overhead and limited precision in the reconstruction process.This study presents a novel lightweight CSI feedback framework known as the dual attention neural network(DANet).Within the DANet architecture,a dual attention module(DAM)is designed to enhance the network's performance.This DAM includes both channel attention blocks and spatial attention blocks.The channel attention blocks direct the model's focus toward channel features rich in information content while simultaneously suppressing less significant features.This approach enables the extraction of temporal correlations within the CSI matrix.The spatial attention block aids in extracting the correlation between the delay domain and the angle domain in the CSI matrix.By enhancing neural network performance,the DAM reduces information dispersion while enhancing the representation of global interactions.Simulation results demonstrate that DANet exhibits superior normalized mean square error and cosine similarity with comparable complexity compared to existing advanced CSI feedback methods.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240319003the NSFC under Grant No.62571112。
摘要To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especially in the frequency division duplex(FDD)systems.However,due to the enormous number of antennas in massive MIMO systems,the feedback overhead of downlink CSI acquisition is extremely large.To address this issue,deep learning(DL)techniques have been introduced to de velop high-accuracy feedback strategies under limited backhaul constraints.In this paper,we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems.Specifically,we introduce the conventional CSI compression and feedback schemes and the existing problems.Besides,we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques.We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback.In addition,we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.
基金supported by the National Natural Science Foundation of China(Nos.62071482 and 62471348)the Shaanxi Association of Science and Technology Youth Talent Support Program Project,China(No.20230137)+1 种基金the Innovative Talents Cultivate Program for Technology Innovation Team of Shaanxi Province,China(No.2024RS-CXTD-08)the Youth Innovation Team of Shaanxi Universities,China。
摘要Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of managing finite transmit and receive antennas and transmit power systematically to enhance detection performance.To tackle the multidimensional resource optimization challenge,we introduce a Cooperative Transmit-Receive Antenna Selection and Power Allocation(CTRSPA)strategy.It employs a perception-action cycle that incorporates uncertain external support information to optimize worst-case detection performance with multiple targets.First,we derive a closed-form expression that incorporates uncertainty for the noncoherent integration squared-law detection probability using the Neyman-Pearson criterion.Subsequently,a joint optimization model for antenna selection and power allocation in CFAR detection is formulated,incorporating practical radar resource constraints.Mathematically,this represents an NPhard problem involving coupled continuous and Boolean variables.We propose a three-stage method—Reformulation,Node Picker,and Convex Power Allocation—that capitalizes on the independent convexity of the optimization model for each variable,ensuring a near-optimal result.Simulations confirm the approach's effectiveness,efficiency,and timeliness,particularly for large-scale radar networks,and reveal the impact of threat levels,system layout,and detection parameters on resource allocation.
基金supported by the National Natural Science Foundation of China(NSFC)under grant 62125108the National Science and Technology Major Project-Mobile Information Networks under Grant No.2024ZD1300700.
摘要Multiple-input multiple-output(MIMO)systems are essential for improving capacity and reliability in semantic communications.Existing methods mainly design the channel-aware neural networks but neglect the underlying signal distribution.In this paper,we develop a denoising diffusion null-space model-based module over MIMO channels(DDNM-MIMO),which is a plug-in module deployed at the receiver.By modeling the MIMO channel,precoding,and equalization as a linear transformation with additive noise,we design corresponding linear and scaling matrices to construct a sampling process for denoising the received signal.The DDNM-MIMO integrates channel state information(CSI)embedding,supporting both closed-loop MIMO with CSI at the transmitter and open-loop MIMO with CSI at the receiver,thereby improving channel adaptability across various noise levels.As a plug-in,the DDNM-MIMO module operates independently of the joint source-channel coding(JSCC)coder structure,offering flexible integration into diverse systems.Experimental results show that DDNM-MIMO effectively reduces the mean square errors(MSE)between the encoded and equalized signals.Consequently,the proposed DDNM-MIMO semantic communication system achieves superior image reconstruction performance compared to existing JSCC-based semantic communication method.