Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications

2025-12-04

AI Summary

To address the low estimation accuracy and high computational complexity in channel estimation for ultra-massive MIMO systems, this paper proposes a beam-domain Bayesian channel estimation algorithm jointly constrained by low-rank and sparsity priors. Innovatively, it unifies the beam-domain low-rankness and sparsity of the channel within a Bayesian framework. An expectation-maximization (EM)-based iterative optimization method is developed, integrating sparse Bayesian learning with soft-thresholding gradient descent to enable efficient reconstruction of non-stationary channels. Compared with state-of-the-art approaches, the proposed algorithm achieves significantly improved estimation accuracy across diverse signal-to-noise ratios while reducing computational complexity. Theoretical analysis ensures rigorous Bayesian inference, and the algorithm’s design emphasizes practical implementation feasibility. Experimental results validate its superior trade-off between accuracy and efficiency, demonstrating both theoretical soundness and engineering applicability in ultra-massive MIMO scenarios.

Technology Category

• Machine Learning: Bayesian Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This letter investigates channel estimation for ultra-massive multiple-input multiple-output (MIMO) communications. We propose a joint low-rank and sparse Bayesian estimation (LRSBE) algorithm for spatial non-stationary ultra-massive channels by exploiting the low-rankness and sparsity in the beam domain. Specifically, the channel estimation integrates sparse Bayesian learning and soft-threshold gradient descent within the expectation-maximization framework. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art alternatives under different signal-to-noise ratio conditions in terms of estimation accuracy and overall complexity.

Problem

• Estimates ultra-massive MIMO channels using low-rank and sparse properties
• Integrates Bayesian learning with gradient descent for improved accuracy
• Reduces complexity while enhancing channel estimation performance

Innovation

• Joint low-rank and sparse Bayesian channel estimation algorithm
• Integrates sparse Bayesian learning with gradient descent
• Uses expectation-maximization framework for improved accuracy