OSCAR: A Contention Window Optimization Approach Using Deep Reinforcement Learning

The contention window (CW) has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the basic access method employed by 802.11 networks does not scale well for increasing number of stations. Therefore, in this paper we propose a new CW control method which leverages Deep Reinforcement Learning (DRL) to learn the optimal policies under different network conditions. For this reason, we propose the Online Smart Collision Avoidance Reinforcement learning (OSCAR) algorithm, a DRL-based algorithm that can be deployed online to quickly and efficiently find the best contention window that maximizes the throughput. We also demonstrate through a simulation campaign that it is able to learn the optimal policies way faster than the current state of art methods while also being able to keep the computational cost low.