Fundamental Limits of CSI Compression in FDD Massive MIMO

2026-03-15

AI Summary

This work addresses the challenge of efficiently compressing wideband channel state information (CSI) in FDD massive MIMO systems, where high-dimensional CSI is difficult to compress due to its intrinsic complexity. The authors propose a Gaussian Mixture Transform Coding (GMTC) framework that models CSI as a Gaussian mixture source with latent geometric states, integrating state inference with state-adaptive Karhunen–Loève transform for effective compression. Theoretical analysis establishes the fundamental rate-distortion limit for CSI compression and introduces a global reverse water-filling bit allocation strategy. Notably, the proposed method achieves performance close to the theoretical limit without relying on large neural networks, outperforming existing neural transform coding schemes on the COST2100 dataset while requiring significantly lower model complexity and memory overhead.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Information Theory
• Data Mining & Knowledge Management: Data Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Channel state information (CSI) feedback in frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems is fundamentally limited by the high dimensionality of wideband channels. In this paper, we model the stacked wideband CSI vector as a Gaussian-mixture source with a latent geometry state that represents different propagation environments. Each component corresponds to a locally stationary regime characterized by a correlated proper complex Gaussian distribution with its own covariance matrix. This representation captures the multimodal nature of practical CSI datasets while preserving the analytical tractability of Gaussian models. Motivated by this structure, we propose Gaussian-mixture transform coding (GMTC), a practical CSI feedback architecture that combines state inference with state-adaptive TC. The mixture parameters are learned offline from channel samples and stored as a shared statistical dictionary at both the user equipment (UE) and the base station. For each CSI realization, the UE identifies the most likely geometry state, encodes the corresponding label using a lossless source code, and compresses the CSI using the Karhunen-Loeve transform matched to that state. We further characterize the fundamental limits of CSI compression under this model by deriving analytical converse and achievability bounds on the rate-distortion (RD) function. A key structural result is that the optimal bit allocation across all mixture components is governed by a single global reverse-waterfilling level. Simulations on the COST2100 dataset show that GMTC significantly improves the RD tradeoff relative to neural transform coding approaches while requiring substantially smaller model memory and lower inference complexity. These results indicate that near-optimal CSI compression can be achieved through state-adaptive TC without relying on large neural encoders.

Problem

• CSI compression
• FDD Massive MIMO
• rate-distortion
• wideband channels
• channel state information

Innovation

• Gaussian-mixture transform coding
• state-adaptive compression
• rate-distortion bounds
• CSI feedback
• massive MIMO