论文标题
多臂强盗动态光束缩放，用于MMWave对齐和跟踪
Multi-Armed Bandit Dynamic Beam Zooming for mmWave Alignment and Tracking
论文作者
论文摘要
我们提出了一种集成的感应和通信（ISAC）算法，该算法利用了使用最佳武器识别多臂匪徒（MAB）方法来利用层次结构代码书的结构，以初步对齐和跟踪移动实体（ME）。该算法称为动态光束缩放（DBZ），进行了光束调整，以减轻与无线MMWAVE系统相关的严重中断，并允许对管理通信的参数进行自适应控制。我们分析了DBZ的样本复杂性，并使用它来告知算法如何根据ME运动和信噪比（SNR）适应非平稳的MAB统计。我们进行广泛的模拟来验证该方法，并证明DBZ在不需要通道多路径或褪色知识的情况下与现有的贝叶斯算法具有竞争力。特别是，DBZ在低SNR制度中的其他低复杂算法优于其他低复合算法。我们还使用NYU SIM来说明DBZ在标准化的农村和城市场景中的功效。
We propose an Integrated Sensing and Communication (ISAC) algorithm that exploits the structure of a hierarchical codebook of beamforming vectors using a best-arm identification Multi-Armed Bandit (MAB) approach for initial alignment and tracking of a Mobile Entity (ME). The algorithm, called Dynamic Beam Zooming (DBZ), performs beam adjustments that mitigate the severe outages associated with wireless mmWave systems and allow for adaptive control of the parameters governing communications. We analyze the sample complexity of DBZ and use it to inform how the algorithm adapts to the nonstationary MAB statistics based on ME motion and Signal-to-Noise Ratio (SNR). We perform extensive simulations to validate the approach and demonstrate that DBZ is competitive against existing Bayesian algorithms, without requiring channel multipath or fading knowledge. In particular, DBZ outperforms other low-complexity algorithms in the low SNR regime. We also illustrate the efficacy of DBZ in standardized rural and urban scenarios using NYU Sim.
