Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method

2024-03-05

IEEE Transactions on Signal Processing

AI Summary

Existing channel estimation methods fail for extra-large-scale MIMO (XL-MIMO) under the joint effects of near-field (NF) propagation, double-bandwidth (DB) operation, and spatial non-stationarity (SnS), where conventional channel sparsity structures collapse. Method: This paper proposes a two-stage decoupling framework integrated with a three-layer Bayesian inference mechanism. It introduces a novel structured sparse prior comprising angular-domain block sparsity, spatially non-stationary modeling, and subchannel signal decoupling—specifically tailored to spherical-wave NF channels—and designs the first three-layer generalized approximate message passing (TL-GAMP) algorithm for such channels. Results: The proposed method achieves stable convergence across diverse channel regimes—including NF-SnS, NF-stationary (SS), and far-field (FF)-SS—and reduces estimation error by over 30% while maintaining near-linear computational complexity. It significantly enhances both accuracy and scalability of broadband XL-MIMO channel estimation.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Non-convex Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In this paper, the channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of near-field (NF) spherical wavefront effects and spatially non-stationary (SnS) properties. Due to the diversity of SnS characteristics across different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase estimation scheme that decouples the problem into multiple subchannel estimation tasks. To solve these sub-tasks, we introduce a novel three-layer Bayesian inference scheme, exploiting the correlations and sparsity of the SnS subchannels in both the spatial and angular domains. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS properties in the spatial domain, and the third layer effectively decouples each subchannel from the observed signal. To enable efficient Bayesian inference, we develop a three-layer generalized approximate message passing (TL-GAMP) algorithm that combines structured variational message passing with belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed TL-GAMP algorithm, demonstrating its robustness across various channel environments, including NF-SnS, NF spatially stationary (NF-SS), and far-field spatially stationary (FF-SS) scenarios.

Problem

• Addressing near-field effects in wideband XL-MIMO channel estimation
• Analyzing dual-wideband impacts on sparsity patterns in XL-MIMO
• Developing efficient Bayesian inference for XL-MIMO channel estimation

Innovation

• Leverages spatial-chirp for sparsity characterization
• Uses MMV-based Bayesian inference model
• Develops MMV-VMP algorithm for reconstruction