网络中分布式学习：一种多人多臂Bandit框架

• 2020 年 4 月 3 日
• 筆記

新一代网络将变得十分的密集，并且峰率十分高，但预计每个用户的平均流量则会相对低。因此，现有的基于资源分配的中央控制器也许会引发大量的信号传递（控制通信），给服务质量（例如：通信中断）以及能量和频谱效率带来负面影响。

为了克服这一问题，人们想出了认知 Ad Hoc网络（CAHN）。这种网络能与其他网络分享频谱， 允许用户在“自由槽”中确认与交流，以此来减少信令负荷，增加每个基站（密集网络）中用户的数量。这些网络带来了许多有趣的挑战，例如资源鉴别、协调、动态和情境感知适应。而机器自学习和人工智能框架为我们提供了许多全新的解决方案。

本篇论文讨论了最先进的多臂多人bandit框架。这种框架基于分布式学习算法，能让用户使用环境并且与其他人/用户协调。论文还讨论了各种为实现CAHN的开放研究问题，以及CAHN在其他领域的有趣应用，例如能量收集、物联网和智能电网。

原文标题：Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework Ad Hoc

原文：Next-generation networks are expected to be ultra-dense with a very high peak rate but relatively lower expected traffic per user. For such scenario, existing central controller based resource allocation may incur substantial signaling (control communications) leading to a negative effect on the quality of service (e.g. drop calls), energy and spectrum efficiency.

To overcome this problem, cognitive ad-hoc networks (CAHN) that share spectrum with other networks are being envisioned. They allow some users to identify and communicate in `free slots' thereby reducing signaling load and allowing the higher number of users per base stations (dense networks). Such networks open up many interesting challenges such as resource identification, coordination, dynamic and context-aware adaptation for which Machine Learning and Artificial Intelligence framework offers novel solutions.

In this paper, we discuss state-of-the-art multi-armed multi-player bandit based distributed learning algorithms that allow users to adapt to the environment and coordinate with other players/users. We also discuss various open research problems for feasible realization of CAHN and interesting applications in other domains such as energy harvesting, Internet of Things, and Smart grids.

原文作者：Sumit J. Darak, Manjesh K.Hanawal

原文链接：https://arxiv.org/abs/2004.00367