Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning
This addresses scalability issues in Wi-Fi networks for users, but it is incremental as it builds on existing DRL techniques.
The paper tackles the problem of inefficient contention window settings in IEEE 802.11ax Wi-Fi networks by proposing a deep reinforcement learning-based method, achieving near-optimal efficiency with low computational cost in simulations.
The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Therefore, we propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Our method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.