Papers › M-Net: A Lightweight Network Based on Multilayer Perceptron for Massive MIMO CSI Feedback
M-Net: A Lightweight Network Based on Multilayer Perceptron for Massive MIMO CSI Feedback
Yaxin Yu, Yinglei Teng, Binghui Wang, An Liu, Vincent Lau
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections