基于贝叶斯多臂Bandits的次优波束对准
Second-best Beam-Alignment via Bayesian Multi-Armed Bandits
摘要：
毫米波（mm波）系统依赖于窄波束来应对mm波频段中严重的信号衰减。然而，由于移动性或阻塞而对波束未对准的敏感性要求使用波束对准方案，在开销和系统资源的使用方面具有巨大的成本。本文提出了一种基于贝叶斯多臂强盗的波束对准方案，目标是最大化对准概率和数据通信吞吐量。提出了一种贝叶斯方法，通过考虑状态作为后验分布的角度的到达（AoA）和出发（AoD），给定的历史反馈信令和波束对扫描的基站（BS）和用户端（UE）。以BS-UE波束对的偏好的形式导出用于最优控制的简化的充分统计。通过约束一个价值函数，推导出次优偏好策略，该策略通过选择具有当前次优偏好的波束对来达到探测和利用之间的最优平衡.通过模拟波束形成的Monte-Carlo仿真，第二最佳偏好政策的优越性能被证明在现有的计划相比，基于第一最佳偏好，线性汤普森采样，和置信上限，与高达7%，10%和30%的提高对齐概率，分别。
英文摘要：
Millimeter-wave (mm-wave) systems rely on narrow-beams to cope with the severe signal attenuation in the mm-wave frequency band. However, susceptibility to beam mis-alignment due to mobility or blockage requires the use of beam-alignment schemes, with huge cost in terms of overhead and use of system resources. In this paper, a beam-alignment scheme is proposed based on Bayesian multi-armed bandits, with the goal to maximize the alignment probability and the data-communication throughput. A Bayesian approach is proposed, by considering the state as a posterior distribution over angles of arrival (AoA) and of departure (AoD), given the history of feedback signaling and of beam pairs scanned by the base-station (BS) and the user-end (UE). A simplified sufficient statistics for optimal control is derived, in the form of preference of BS-UE beam pairs. By bounding a value function, the second-best preference policy is derived, which strikes an optimal balance between exploration and exploitation by selecting the beam pair with the current second-best preference. Through Monte-Carlo simulation with analog beamforming, the superior performance of the second-best preference policy is demonstrated in comparison to existing schemes based on first-best preference, linear Thompson sampling, and upper confidence bounds, with up to 7%, 10% and 30% improvements in alignment probability, respectively.
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。
