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
基金National Natural Science Foundation of China(12005108)。
文摘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.
