论文标题
无线多访问策略及其信号的可扩展联合学习
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling
论文作者
论文摘要
在本文中，我们应用了一个多代理增强学习（MARL）框架，允许基站（BS）和用户设备（UES）共同学习频道访问策略及其在无线的多个访问方案中的信号。在此框架中，BS和UES是需要合作才能提供数据的增强剂学习（RL）代理。与无争议的基线和基于争论的基线的比较表明，即使在高流量情况下，我们的框架在高速公路方面也取得了出色的性能，同时保持低碰撞率。研究了所提出的方法的可伸缩性，因为它是MARL中的主要问题，本文提供了第一个结果以解决它。
In this paper, we apply an multi-agent reinforcement learning (MARL) framework allowing the base station (BS) and the user equipments (UEs) to jointly learn a channel access policy and its signaling in a wireless multiple access scenario. In this framework, the BS and UEs are reinforcement learning (RL) agents that need to cooperate in order to deliver data. The comparison with a contention-free and a contention-based baselines shows that our framework achieves a superior performance in terms of goodput even in high traffic situations while maintaining a low collision rate. The scalability of the proposed method is studied, since it is a major problem in MARL and this paper provides the first results in order to address it.
