CSI Compression for Massive MIMO-OFDM: Mismatch-Aware Rate-Distortion Trade-offs

TL;DR

This paper develops a mismatch-aware rate-distortion framework for CSI compression in massive MIMO-OFDM systems, improving robustness and performance under covariance model mismatch.

Contribution

It introduces a robust reverse water-filling allocation method that accounts for covariance mismatch, enhancing CSI reconstruction quality.

Findings

RRWF outperforms conventional RWF under covariance mismatch.

Simulations demonstrate improved mean square error with RRWF.

Decoupling across modes simplifies the allocation problem.

Abstract

We study channel state information (CSI) compression for wideband frequency division duplex massive multiple-input multiple-output (MIMO) when the base station (BS) reconstructs CSI using an imperfect covariance model. Under matched second-order statistics, remote rate--distortion theory yields transform coding with reverse water-filling (RWF) over covariance eigenmodes. With decoder-side covariance mismatch, however, this allocation is no longer end-to-end optimal. We derive an achievable mismatched Gaussian rate--distortion characterization based on a Gaussian test channel and a mismatched minimum mean square error (MMSE) reconstruction rule. In a shared-eigenvector regime (common eigenbasis, mismatched eigenvalues), the problem decouples across modes and leads to a robust reverse water-filling (RRWF) allocation computable via bisection and per-mode root finding. Simulations using wideband massive MIMO covariance models show that RRWF consistently improves reconstruction distortion and end-to-end mean square error relative to conventional RWF under mismatch.