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

2026-04-19

AI Summary

This work addresses the degradation in channel state information (CSI) compression performance in massive MIMO-OFDM systems caused by covariance model mismatch at the base station. By leveraging a Gaussian test channel and a mismatched minimum mean square error (MMSE) reconstruction criterion, the authors derive an achievable mismatched Gaussian rate-distortion function and, for the first time, achieve mode decoupling under a setting where eigenvectors are shared but eigenvalues are mismatched. Building on this analysis, they propose a computable Robust Reverse Water-Filling (RRWF) resource allocation algorithm that overcomes the suboptimality of conventional approaches in mismatched scenarios. Simulations demonstrate that, in wideband massive MIMO settings, RRWF significantly outperforms traditional reverse water-filling strategies, effectively reducing both CSI reconstruction distortion and end-to-end mean square error.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Applications
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

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.

Problem

• CSI compression
• massive MIMO-OFDM
• covariance mismatch
• rate-distortion trade-off
• channel state information

Innovation

• CSI compression
• covariance mismatch
• robust reverse water-filling
• rate-distortion theory
• massive MIMO-OFDM