基于线性老虎机的高效自适应近场波束训练
Efficient, Adaptive Near-Field Beam Training based on Linear Bandit
AI总结：
本文提出基于线性老虎机的近场波束训练方法，通过自适应平衡探索与利用，减少导频开销并提升SNR增益。
摘要：
本文提出了一种基于线性老虎机的近场通信波束训练框架，用于多径信道环境。通过利用汤普森采样（TS），该框架能够自适应地平衡探索与利用，以在有限的导频开销下最大化累积波束成形增益。为了确保数据高效学习，我们引入了在DFT域中的相关高斯先验，使用高斯核来捕捉空间相关性和近场能量泄漏。我们开发了三种TS策略：用于通过结构正则化实现快速收敛的码本约束搜索，用于实现近最优性能的连续空间搜索，以及一种两阶段混合细化方案，该方案在收敛速度和估计精度之间取得平衡。仿真结果表明，所提出的框架在多径环境中将导频开销减少了高达90%，并且在SNR增益方面超过基线方法超过2dB。此外，连续空间搜索被证明是渐近最优的，当导频开销不受限制时，它接近全CSI界限。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
