Compressive Beam-Pattern-Aware Near-field Beam Training via Total Variation Denoising

TL;DR

This paper introduces a novel near-field beam training method that combines LASSO with total variation denoising to accurately estimate beam patterns in large antenna arrays for 6G, reducing overhead and preserving beam features.

Contribution

It proposes a beam-pattern-preserving scheme that enhances near-field beam training by integrating LASSO with TV denoising, avoiding complex codebook design and improving accuracy.

Findings

Consistent NMSE improvements over least squares and LASSO.

Effective preservation of beam pattern plateaus and edges.

Reduced pilot overhead in near-field beam training.

Abstract

Extremely large antenna arrays envisioned for 6G incurs near-field effect, where steering vector depends on angles and range simultaneously. Polar-domain near-field codebooks can focus energy accurately but incur extra two-dimensional sweeping overhead; compressed-sensing (CS) approaches with Gaussian-masked DFT sensing offer a lower-overhead alternative. This letter revisits near-field beam training using conventional DFT codebooks. Unlike far-field responses that concentrate energy on a few isolated DFT beams, near-field responses produce contiguous, plateau-like energy segments with sharp transitions in the DFT beamspace. Pure LASSO denoising, therefore, tends to over-shrink magnitudes and fragment plateaus. We propose a beam-pattern-preserving beam training scheme for multiple-path scenarios that combines LASSO with a lightweight denoising pipeline: LASSO to suppress small-amplitude noise, followed by total variation (TV) to maintain plateau levels and edge sharpness. The two proximal steps require no near-field codebook design. Simulations with Gaussian pilots show consistent NMSE and cosine-similarity gains over least squares and LASSO at the same pilot budget.