This paper proposes a robust adversarial reinforcement learning (RARL)-based multi-access point (AP) coordination method that is robust even against unexpected decentralized operations of uncoordinated APs. Multi-AP coordination is a promising technique towards IEEE 802.11be, and there are studies that use RL for multi-AP coordination. Indeed, a simple RL-based multi-AP coordination method diminishes the collision probability among the APs; therefore, the method is a promising approach to improve time-resource efficiency. However, this method is vulnerable to frame transmissions of uncoordinated APs that are less aware of frame transmissions of other coordinated APs. To help the central agent experience even such unexpected frame transmissions, in addition to the central agent, the proposed method also competitively trains an adversarial AP that disturbs coordinated APs by causing frame collisions intensively. Besides, we propose to exploit a history of frame losses of a coordinated AP to promote reasonable competition between the central agent and adversarial AP. The simulation results indicate that the proposed method can avoid uncoordinated interference and thereby improve the minimum sum of the throughputs in the system compared to not considering the uncoordinated AP.

中文翻译：

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基于对抗性强化学习的鲁棒接入点协调对抗非协调干扰

本文提出了一种鲁棒的基于对抗性强化学习 (RARL) 的多接入点 (AP) 协调方法，即使对未协调的 AP 的意外分散操作也具有鲁棒性。多 AP 协调是面向 IEEE 802.11be 的一项很有前途的技术，并且有研究使用 RL 进行多 AP 协调。事实上，一个简单的基于 RL 的多 AP 协调方法降低了 AP 之间的冲突概率；因此，该方法是提高时间资源效率的一种很有前途的方法。然而，这种方法容易受到非协作 AP 的帧传输的影响，这些 AP 不太了解其他协作 AP 的帧传输。为了帮助中心代理体验甚至这种意想不到的帧传输，除了中心代理，所提出的方法还竞争性地训练对抗性 AP，该 AP 会通过导致密集的帧冲突来干扰协调的 AP。此外，我们建议利用协调 AP 的帧丢失历史来促进中心代理和对抗 AP 之间的合理竞争。仿真结果表明，与不考虑非协调AP相比，所提出的方法可以避免非协调干扰，从而提高系统中吞吐量的最小总和。