Deep Reinforcement Learning based Rate Adaptation for Wi-Fi Networks

The rate adaptation (RA) algorithm, which adaptively selects the rate according to the quality of the wireless environment, is one of the cornerstones of the wireless systems. In Wi-Fi networks, dynamic wireless environments are mainly due to fading channels and collisions caused by random access protocols. However, existing RA solutions mainly focus on the adaptive capability of fading channels, resulting in conservative RA policies and poor overall performance in highly congested networks. To address this problem, we propose a model-free deep reinforcement learning (DRL) based RA algorithm, named as drlRA, in this work, which incorporates the impact of collisions into the reward function design. Numerical results show that the proposed algorithm improves the throughput by 16.5% and 39.5% while reducing the latency by 25% and 19.3% compared to state-of-the-art baselines.