Sensing-aided CSI Feedback with Deep Learning for Massive MIMO Systems

For frequency division duplexing massive multiple-input multiple-output systems, downlink channel state information (CSI) is required to be compressed and fed back to the base station (BS) to support beamforming. Recently, deep learning (DL) has demonstrated overwhelming performance in CSI feed-back, wherein multimodal information is explored to further improve the performance. With the emergence of integrated sensing and communications, radar-equipped BSs exhibit the capability to sense the wireless environment and assist communication design. In this paper, we propose a sensing-aided DL-based CSI feedback method, in which the angle information of scatters in the communication channel is sensed by the BS and utilized to reduce feedback overhead. A novel two-stage feedback scheme with lightweight network structures is carefully designed to improve feedback performance. Experiments demonstrate that compared to previous methods without utilization of sensing information, our sensing-aided methods significantly enhance performance in low-bit scenarios with reduced computational complexity. The open-source codes are available at https://github.com/zhang-xd18/safb.