Enhancing Deep Learning Performance of Massive MIMO CSI Feedback

Abstract--CSI feedback is an important problem of massive multiple-input multiple-output (MIMO) technology because the feedback overhead is proportional to the number of sub-channels and the number of antennas, both of which scale with the size of the massive MIMO system.Deep learning-based CSI feedback methods have been widely adopted recently owing to their superior performance. Despite the success, current approaches have not fully exploited the relationship between the characteristics of CSI data and the deep learning framework. Generally, DL-based methods utilize systems, e.g., 5G and above. Unlike traditional cellbased the auto-encoder framework [7], where the encoder learns to communication paradigms, the massive MIMO makes compress the original CSI at the UE side and the decoder better use of spatial diversity and serves users in a cellfree learns to reconstruct the original CSI at the BS side. A massive MIMO system typically is equipped is trained in unsupervised manner without the need with a large number of antennas at the base station (BS), for labeled data and only requires a single run upon deployment which aims to make full use of spatial diversity by conducting for continuous CSI reconstruction, which overcomes the beamforming to concentrate signal energy to a specific user computation inefficiency of traditional CS-based approaches.