Efficient, Adaptive Near-Field Beam Training based on Linear Bandit

TL;DR

This letter proposes a linear bandit-based beam training framework for near-field communication under multi-path channels that adaptively balances exploration and exploitation to maximize cumulative beamforming gain under limited pilot overhead and incorporates a correlated Gaussian prior in the DFT domain to ensure data-efficient learning.

Abstract

This letter proposes a linear bandit-based beam training framework for near-field communication under multi-path channels. By leveraging Thompson Sampling (TS), the framework adaptively balances exploration and exploitation to maximize cumulative beamforming gain under limited pilot overhead. To ensure data-efficient learning, we incorporate a correlated Gaussian prior in the DFT domain, using a Gaussian kernel to capture spatial correlations and near-field energy leakage. We develop three TS strategies: codebook-constrained search for rapid convergence via structural regularization, continuous-space search to achieve near-optimal performance, and a two-stage hybrid refinement scheme that balances convergence speed and estimation accuracy. Simulation results show that the proposed framework reduces pilot overhead by up to 90\% while achieving more than a 2dB SNR gain over baselines in multipath environments. Furthermore, the continuous-space search is shown to be asymptotically optimal, approaching the full-CSI bound when the pilot overhead is unconstrained.

Figures (5)

• Figure 1: Diagram of the near-field communication system featuring a ULA.
• Figure 2: Influence of power leakage on the angular domain and the corresponding RBF kernel.
• Figure 3: Achievable rate performance vs. SNR of various schemes under finite maximum pilot overhead constraints.
• Figure 4: Beam Training Pilot Overhead versus SNR of various schemes.
• Figure 5: Achievable rate versus SNR: Comparison between the unconstrained Scheme II and various schemes under finite pilot overhead constraints.