Bayesian Learning-Aided Near-Field Channel Estimation for mmWave Hybrid MIMO systems employing Uniform Circular Array

TL;DR

This paper introduces a Bayesian learning framework for near-field channel estimation in mmWave MIMO systems with UCAs, improving accuracy and scalability over existing methods.

Contribution

It proposes a novel Ring-Bayes framework combined with a concentric-ring codebook for accurate near-field channel estimation with UCAs.

Findings

Significant performance improvements over existing methods

Effective near-field channel recovery in large-scale systems

Scalability demonstrated through extensive simulations

Abstract

This work conceives a Ring-Bayes channel learning framework that unifies Bayesian learning with near-field channel estimation in millimeter-wave (mmWave) hybrid MIMO systems. As the number of antennas scales up, users increasingly fall within the near-field region, rendering the conventional planar-wave assumption invalid. Moreover, the widely studied uniform linear arrays (ULAs) at the base station are impractical for large-scale deployment, whereas uniform circular arrays (UCAs) achieve superior beamforming gain and spatial directivity with the same antenna aperture. To exploit these advantages, we design a near-field concentric-ring codebook that captures channel features jointly in angular and distance domains. Leveraging this structure, the proposed Ring-Bayes framework enables highly accurate recovery of UCA near-field channels. Extensive simulations confirm that our approach delivers substantial improvements over existing methods, establishing Ring-Bayes as a powerful and scalable solution for next-generation mmWave communications.