arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.00835cs.NI
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基于对抗性强化学习的抗不协调干扰鲁棒接入点协调

Adversarial Reinforcement Learning-based Robust Access Point Coordination Against Uncoordinated Interference

Yuto Kihira, Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura

摘要:

本文提出了一种基于对抗性强化学习(RARL)的多接入点(AP)协调方法,该方法即使在不协调AP的意外分散操作下也具有鲁棒性。多AP协调是IEEE 802.11be的一种有前途的技术,并且存在使用RL进行多AP协调的研究。事实上,一个简单的基于RL的多AP协调方法减少了AP之间的冲突概率,因此,该方法是一个有前途的方法,以提高时间资源效率。然而,该方法易受不太了解其他协调AP的帧传输的不协调AP的帧传输的影响。为了帮助中央代理体验甚至这样的意外帧传输,除了中央代理之外,所提出的方法还竞争性地训练对抗性AP,该AP通过引起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,以促进中央代理和对抗AP之间的合理竞争。仿真结果表明,与不考虑非协调AP的情况相比,该方法可以避免非协调干扰,从而提高系统的最小吞吐量和。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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