Carrier sensing multiple access/collision avoidance (CSMA/CA) is the backbone MAC protocol for IEEE 802.11 networks. However, tuning the binary exponential back-off (BEB) mechanism of CSMA/CA in user-dense scenarios so as to maximize aggregate throughput still remains a practically essential and challenging problem. In this paper, we propose a new and enhanced multiple access mechanism based on the application of deep reinforcement learning (DRL) and Federated learning (FL). A new Monte Carlo (MC) reward updating method for DRL training is proposed and the access history of each station is used to derive a DRL-based MAC protocol that improves the network throughput vis-a-vis the traditional distributed coordination function (DCF). Further, federated learning (FL) is applied to achieve fairness among users. The simulation results showcase that the proposed federated reinforcement multiple access (FRMA) performs better than basic DCF by 20% and DCF with request-to-send/clear-to-send (RTS/CTS) by 5% while guaranteeing the fairness in user-dense scenarios.

中文翻译：

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通过联合深度强化学习增强WiFi多路访问性能

载波侦听多路访问/避免冲突（CSMA / CA）是IEEE 802.11网络的骨干MAC协议。但是，在用户密集型方案中调整CSMA / CA的二进制指数退避（BEB）机制以使总吞吐量最大化仍然是一个实际上必不可少且具有挑战性的问题。在本文中，我们基于深度强化学习（DRL）和联合学习（FL）的应用，提出了一种新的增强型多访问机制。提出了一种新的用于DRL训练的蒙特卡洛（MC）奖励更新方法，并使用每个站的访问历史来导出基于DRL的MAC协议，该协议相对于传统的分布式协调功能（DCF）改善了网络吞吐量。此外，应用联合学习（FL）来实现用户之间的公平。