TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks |
| 영문제목(English Title) |
Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks |
| 저자(Author) |
Chih-Heng Ke
Lia Astuti
|
| 원문수록처(Citation) |
VOL 16 NO. 01 PP. 0334 ~ 0349 (2022. 01) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
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 single-users 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 (𝐶W_Threshold). 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. |
| 키워드(Keyword) |
Contention window threshold (𝐶W_Threshold)
deep Q-learning network (DQN)
deep reinforcement learning (DRL)
wireless networks
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| 파일첨부 |
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