Active-Sensing-Based Beam Alignment for Near Field MIMO Communications

TL;DR

This paper introduces an active-sensing learning algorithm utilizing wavenumber-domain transform matrices to efficiently perform near-field beam alignment in MIMO systems, reducing training overhead and accelerating convergence.

Contribution

It presents a novel active-sensing algorithm using WTMs for near-field beam alignment, improving speed and reducing training compared to traditional codebook methods.

Findings

The proposed method accelerates convergence in beam alignment.

It reduces training overhead by avoiding beam sweeping.

Numerical results confirm the effectiveness of the approach.

Abstract

An active-sensing-based learning algorithm is proposed to solve the near-field beam alignment problem with the aid of wavenumber-domain transform matrices (WTMs). Specifically, WTMs can transform the antenna-domain channel into a sparse representation in the wavenumber domain. The dimensions of WTMs can be further reduced by exploiting the dominance of line-of-sight (LoS) links. By employing these lower-dimensional WTMs as mapping functions, the active-sensing-based algorithm is executed in the wavenumber domain, resulting in an acceleration of convergence. Compared with the codebook-based beam alignment methods, the proposed method finds the optimal beam pair in a ping-pong fashion, thus avoiding high training overheads caused by beam sweeping. Finally, the numerical results validate the effectiveness of the proposed method.