Model-Driven Deep Learning Based Precoding for FDD Cell-Free Massive MIMO with Imperfect CSI

This paper proposes a model-driven deep learning based channel feedback and multi-user precoding scheme for cell-free massive MIMO systems, where the downlink pilot signals, CSI compressor (from received pilots to quantized bits) at user equipments (UEs), CSI reconstruction at BSs, and multi-user precoding are designed. Specifically, based on the proposed Transformer-based auto-encoder, the non-orthogonal downlink pilots from different BSs, the CSI compressor at UEs, and the CSI reconstruction (from bits to CSI matrix) at different BSs are end-to-end trained in a distributed manner. Moreover, by utilizing the angular-domain reciprocity of downlink/uplink channels, the CSI reconstruction at the BSs can be further improved with the aid of uplink CSI, which can be easily obtained at the UEs' initial access stage. Additionally, we propose a model-driven deep unfolding based multi-user precoding by unfolding the conventional zero-forcing algorithm and integrating learnable parameters, which substantially reduces the computational complexity and improves the robustness to imperfect CSI.