網路中分散式學習：一種多人多臂Bandit框架

• 2020 年 4 月 3 日
• 筆記

新一代網路將變得十分的密集，並且峰率十分高，但預計每個用戶的平均流量則會相對低。因此，現有的基於資源分配的中央控制器也許會引發大量的訊號傳遞（控制通訊），給服務品質（例如：通訊中斷）以及能量和頻譜效率帶來負面影響。

為了克服這一問題，人們想出了認知 Ad Hoc網路（CAHN）。這種網路能與其他網路分享頻譜， 允許用戶在「自由槽」中確認與交流，以此來減少信令負荷，增加每個基地台（密集網路）中用戶的數量。這些網路帶來了許多有趣的挑戰，例如資源鑒別、協調、動態和情境感知適應。而機器自學習和人工智慧框架為我們提供了許多全新的解決方案。

本篇論文討論了最先進的多臂多人bandit框架。這種框架基於分散式學習演算法，能讓用戶使用環境並且與其他人/用戶協調。論文還討論了各種為實現CAHN的開放研究問題，以及CAHN在其他領域的有趣應用，例如能量收集、物聯網和智慧電網。

原文標題：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

原文鏈接：https://arxiv.org/abs/2004.00367