Channel Knowledge Map-Aided CSI Feedback With PnP Module for Massive MIMO Systems

Abstract

In frequency division duplex (FDD) mode, channel state information (CSI) feedback is crucial for ensuring communication quality in massive multiple-input multiple-output (MIMO) systems. To reduce the feedback overhead, the user equipment (UE) typically compresses the CSI matrix before feeding it back to the base station (BS). Notably, deep learning has been proven well-suited for the CSI feedback task, wherein auxiliary prior information can further enhance CSI compression and reconstruction. However, existing methods ignore such prior information, resulting in suboptimal performance. In the meantime, the channel knowledge map (CKM) has emerged as a key enabler that can provide multi-modal information based on UEs’ positions. In this letter, we propose a CKM-aided CSI feedback scheme. We first introduce a framework for extracting coarse angle and delay information based on the UE’s position and the environmental contour. Then, the angle and delay information are utilized through a plug-and-play (PnP) module to help the decoder focus on the significant region, which is compatible with existing DL-based CSI feedback schemes. Simulation results show that the CKM-PnP module can significantly enhance the decoder’s ability to reconstruct the CSI matrix, especially in high-compression scenarios. For reproducibility, the code is accessible at https://github.com/github-whh/Ckm-CF