Precoding-oriented Massive MIMO CSI Feedback Design

TL;DR

This paper introduces a deep learning-based end-to-end system for CSI feedback in massive MIMO FDD systems, optimizing the tradeoff between feedback overhead and achievable rate performance.

Contribution

It proposes a novel precoding-oriented feedback architecture with learned pilots and compressors, improving efficiency over traditional separate compression and precoding methods.

Findings

Outperforms previous precoding-oriented feedback methods

Achieves higher sum-rate with less feedback overhead

Demonstrates effectiveness through simulation results

Abstract

Downlink massive multiple-input multiple-output (MIMO) precoding algorithms in frequency division duplexing (FDD) systems rely on accurate channel state information (CSI) feedback from users. In this paper, we analyze the tradeoff between the CSI feedback overhead and the performance achieved by the users in systems in terms of achievable rate. The final goal of the proposed system is to determine the beamforming information (i.e., precoding) from channel realizations. We employ a deep learning-based approach to design the end-to-end precoding-oriented feedback architecture, that includes learned pilots, users' compressors, and base station processing. We propose a loss function that maximizes the sum of achievable rates with minimal feedback overhead. Simulation results show that our approach outperforms previous precoding-oriented methods, and provides more efficient solutions with respect to conventional methods that separate the CSI compression blocks from the precoding processing.