Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks
Chih‐Heng Ke, Lia Astuti · KSII Transactions on Internet and Information Systems · 2022
The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters.Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations.Yet, it is still the default when accessing channels for singleusers of 802.11 transmissions.Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL).Its advantage is interacting with the surrounding environment and making decisions based on its own experience.Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards.As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (𝐶𝐶𝐶𝐶 𝑇𝑇ℎ𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑜𝑜𝑜𝑜𝑜𝑜 ).It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios.We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN).The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.