毫米波网络链路配置中的多臂强盗
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks
摘要：
由于不断变化的环境以及毫米波信号对用户移动性和信道条件的高度敏感性，建立和维护毫米波（mmWave）链路具有挑战性。毫米波链路配置问题通常涉及在环境不确定性下从系统硬件和协议支持的有限的备选方案集合中搜索最优系统参数。例如，波束扫描旨在从离散码本中识别用于数据传输的最佳波束。选择诸如波束扫描周期和波束宽度的参数对于实现高的总体系统吞吐量是至关重要的。在这篇文章中，我们鼓励使用多臂强盗（MAB）框架，以智能地搜索出最佳配置时，建立毫米波链路。MAB是一个强化学习框架，它引导决策者从一组动作中顺序选择一个动作。作为一个例子，我们表明，在MAB框架内，最佳波束扫描周期，波束宽度和波束方向可以动态学习与样本计算效率的强盗算法。最后，我们强调了一些未来的研究方向，增强毫米波链路配置设计与MAB。
英文摘要：
Establishing and maintaining millimeter-wave (mmWave) links is challenging due to the changing environment and the high sensibility of mmWave signal to user mobility and channel conditions. MmWave link configuration problems often involve a search for optimal system parameter under environmental uncertainties, from a finite set of alternatives that are supported by the system hardware and protocol. For example, beam sweeping aims at identifying the optimal beam(s) for data transmission from a discrete codebook. Selecting parameters such as the beam sweeping period and the beamwidth are crucial to achieving high overall system throughput. In this article, we motivate the use of the multi-armed bandit (MAB) framework to intelligently search out the optimal configuration when establishing the mmWave links. MAB is a reinforcement learning framework that guides a decision-maker to sequentially select one action from a set of actions. As an example, we show that within the MAB framework, the optimal beam sweeping period, beamwidth, and beam directions could be dynamically learned with sample-computational-efficient bandit algorithms. We conclude by highlighting some future research directions on enhancing mmWave link configuration design with MAB.
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