AI 中文总结
针对混合近/远场传播下RIS信道获取成本高的问题，提出带隐式信道学习的贝叶斯臂波束成形算法，通过汤普森采样等实现高效波束成形，仿真显示其性能优于基线方案且接近CSI基准。
AI 中文摘要
可重构智能表面（RIS）可通过塑造传播环境改善高频无线链路，但无源架构使其信道获取成本高昂。传统的“先估计后优化”方法通常需要与反射元件数量成比例的导频开销，这在短相干时间和混合近/远场传播场景下并不适用。本文提出一种用于RIS相移配置的贝叶斯臂框架，该框架具备隐式信道学习能力。该方法从每个时隙的一个标量导频观测值中更新级联信道的高斯后验分布，并采用汤普森采样平衡信道估计与波束成形增益。我们推导了贝叶斯遗憾分解，将贝叶斯接收功率遗憾与后验不确定性收缩关联起来，进一步建立了条件亚线性贝叶斯遗憾保证。为利用稀疏混合场传播特性，我们开发了能量聚焦角-距离字典以及基于稀疏贝叶斯学习（SBL）的汤普森采样算法，该算法采用热启动超参数优化。仿真结果表明，所提策略在视距（LOS）主导场景下，10个时间块内即可逼近完美信道状态信息（CSI）基准；在多径和瑞利衰落场景下，相比所考虑的基线方案，其传输效率得到提升。
英文摘要
Reconfigurable intelligent surfaces (RISs) can improve high-frequency wireless links by shaping the propagation environment, but their passive architecture makes channel acquisition costly. Conventional estimate-then-optimize methods usually require pilot overhead that scales with the number of reflecting elements, which is undesirable under short coherence times and hybrid near-/far-field propagation. This paper proposes the Bayesian bandit framework for RIS phase-shift configuration with implicit channel learning. The method updates a Gaussian posterior of the cascaded channel from one scalar pilot observation per slot and uses Thompson sampling to balance channel estimation and beamforming gain. We derive a Bayesian regret decomposition that connects Bayesian received-power regret to posterior uncertainty contraction, and further establish a conditional sublinear Bayesian-regret guarantee. To exploit sparse hybrid-field propagation, we develop an energy-focusing angle-distance dictionary and a sparse Bayesian learning (SBL)-based Thompson-sampling algorithm with warm-started hyperparameter refinement. Simulations show that the proposed policies approach the perfect-channel state information (CSI) benchmark in the line-of-sight (LOS)-dominant setting within 10 time block and improve transmission efficiency over the considered baselines in multipath and Rayleigh fading scenarios.
