Cruz, S.C.D.S.; Ahmed Ouameur, M.; Figueiredo, F.A.P.D. Reinforcement Learning-based Wi-Fi Contention Window Optimization. Preprints2022, 2022110011. https://doi.org/10.20944/preprints202211.0011.v1
APA Style
Cruz, S.C.D.S., Ahmed Ouameur, M., & Figueiredo, F.A.P.D. (2022). Reinforcement Learning-based Wi-Fi Contention Window Optimization. Preprints. https://doi.org/10.20944/preprints202211.0011.v1
Chicago/Turabian Style
Cruz, S.C.D.S., Messaoud Ahmed Ouameur and Felipe A. P. de Figueiredo. 2022 "Reinforcement Learning-based Wi-Fi Contention Window Optimization" Preprints. https://doi.org/10.20944/preprints202211.0011.v1
Abstract
The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the expansion of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for static and dynamic scenarios, regardless of the number of stations. Moreover, the performance difference is amplified as the number of stations increases, with DQN and DDPG showing a 27% increase in throughput with 50 stations compared to BEB. Furthermore, DQN and DDPG presented similar performances.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
