M-Net: A Lightweight Network Based on Multilayer Perceptron for Massive MIMO CSI Feedback

원문 초록

In FDD massive multiple-input multiple-output (MIMO) systems, user equipments (UEs) are required to send back the downlink channel state information (CSI) to base stations (BSs). However, the high-dimentional CSI souring proportional to antennas leads to unacceptable feedback overhead. Recently, deep learning (DL) has shown its unique powerful ability to compress and reconstruct CSI. In this paper, a novel Multilayer Perceptron (MLP) based feedback network, named M-Net, is proposed, where the specific spatial correlation of channels is emphasized by tailored feature processing modules. To meet the practical requirements of lightweight networks and ensure low inference latency, M-Net adopts parameter sharing to reduce network complexity and eliminates complex operations. Additionally, to leverage the computational resources at the BSs, we design a scalable decoder to further improve the performance of the network. Simulation results show that the proposed M-Net achieves almost the state-of-the-art (SOTA) performance with the lowest computational complexity, while exhibiting a remarkable 49.63% reduction in the inference delay compared to TransNet. Finally, we demonstrate that the M-Net variants achieve the SOTA performance by only deep enning the M-Net decoder. The open source codes are available at https://github.com/Password-YYX/M-Net.