The exponential increase of wireless devices with highly demanding services such as streaming video, gaming and others has imposed several challenges to Wireless Local Area Networks (WLANs). In the context of Wi-Fi, IEEE 802.11ax brings high-data rates in dense user deployments. Additionally, it comes with new flexible features in the physical layer as dynamic Clear-Channel-Assessment (CCA) threshold with the goal of improving spatial reuse (SR) in response to radio spectrum scarcity in dense scenarios. In this paper, we formulate the Transmission Power (TP) and CCA configuration problem with an objective of maximizing fairness and minimizing station starvation. We present four main contributions into distributed SR optimization using Multi-Agent Multi-Armed Bandits (MAMABs). First, we propose to reduce the action space given the large cardinality of action combination of TP and CCA threshold values per Access Point (AP). Second, we present two deep Multi-Agent Contextual MABs (MA-CMABs), named Sample Average Uncertainty (SAU)-Coop and SAU-NonCoop as cooperative and non-cooperative versions to improve SR. In addition, we present an analysis whether cooperation is beneficial using MA-MABs solutions based on the e-greedy, Upper Bound Confidence (UCB) and Thompson techniques. Finally, we propose a deep reinforcement transfer learning technique to improve adaptability in dynamic environments. Simulation results show that cooperation via SAU-Coop algorithm contributes to an improvement of 14.7% in cumulative throughput, and 32.5% improvement of PLR when compared with no cooperation approaches. Finally, under dynamic scenarios, transfer learning contributes to mitigation of service drops for at least 60% of the total of users.
翻译:在无线局域网(Wi-Fi)中,IEE 802.11ax在密集用户部署方面带来了高数据率。此外,在物理层中出现了新的灵活特征,即动态清晰度评估阈值,目标是在密集情况下改进空间再利用(SR),以应对无线电频谱稀缺。在本文中,我们提出了传输动力和共同国家评估配置问题,目的是最大限度地实现公平并尽量减少站饥饿。在Wi-Fi的背景下,IEE 802.11ax带来高数据率,在密集用户部署方面,高EEE 802.11ax带来高数据率数据率。此外,我们提议减少行动空间,作为动态清晰度评估(CC)的临界值组合,目的是在密集的情况下,改进空间再利用多度背景数据(SR)MAB(MA-CAB),通过SB-OU(SAU-OU)和SOU-OU-OOO(S-OU)的不断改进(S-OOL) 优化方法,作为合作和不透明性(MA-LU-S-LU-LU)的升级技术的升级),最终在学习过程中,改进我们学习中,改进了以信任-S-LU-LU-LU-LU-LU-S-S-S-LU-S-S-LUT-S-S-S-S-S-S-S-S-LU-LU-S-S-S-S-S-S-S-S-LU-Lislental-S-Lislental-SLisl)的升级技术,这是最后的学习的升级的升级的升级,这是最后的升级的升级的升级技术分析,最终的升级,这是最终的升级的升级的升级的升级的升级的学习的升级,最终方法,这是最后的升级的学习,这是的升级的升级的升级的升级的升级的升级的升级,这是的升级的升级的升级,这是最后的学习的学习的升级的学习的学习的学习的升级,这是最后的学习的升级,这是的学习的升级的学习的升级的升级,这是最后的升级的升级的升级的升级的升级的升级的升级的升级,这是最后的升级的升级的升级的升级