Abstract:This paper proposes a posterior sampling-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 the final beamforming gain under a finite pilot overhead. To ensure data-efficient learning, we incorporate a structured Gaussian prior in the DFT domain and use an RBF covariance as a tractable local-correlation prior, motivated by near-field energy leakage, to promote information sharing among neighboring DFT components. We develop three TS strategies: codebook-constrained search for rapid stabilization via structural regularization, continuous-space search for high accuracy refinement, and a two-stage hybrid refinement scheme that balances training efficiency and estimation accuracy. Simulation results show that the proposed framework reduces pilot overhead by over 90\% while achieving more than a 2 dB SNR gain over baselines in multipath environments. Furthermore, simulations show that the continuous-space search approaches the full-CSI benchmark as the available pilot numbers become sufficiently large.
From: Junchi Liu [view email]
[v1]
Tue, 10 Mar 2026 16:49:48 UTC (1,808 KB)
[v2]
Sat, 22 Aug 2026 04:26:21 UTC (1,096 KB)
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