DRL-Empowered Wi-Fi Channel Access in Future Intelligent Network

With the growing demand for next-generation wireless networks and the rapid development of AI, traditional Wi-Fi MAC design challenges to manage the increasingly complex network optimization, and the machine learning-integrated MAC (MLMAC) initiative we work on is expected to contribute to the future intelligent network. This paper studies deep reinforcement learning (DRL)-empowered Wi-Fi distributed channel access (DCA) strategy, introducing the concept of network access entropy to effectively quantify DCA chaotic degree and facilitate ML-MAC performance analysis. We focus on the centralized training decentralized execution (CTDE) paradigm, e.g. the algorithm QMIX, in the multiagent reinforcement learning (MARL) framework and formulate decentralized-partially observable Markov decision process (DECPOMDP) in DCA cooperation network. We consider the tradeoff between total and individual rewards and first introduce wait decision counter (WDC) as part of the DCA agent observation, enabling deep neural network (DNN) to achieve better performance and convergence stability. Extensive simulation results on our MLMAC protocol stack platform with NS3 and PyTorch demonstrate the strategy's superiority over CSMA/CA under both unsaturated and saturated traffic, as well as the advantage revelation of i) trained-network generalization; ii) dynamic access robustness; iii) protocol heterogeneity fairness.