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Performance Enhancement of CSMA/CA MAC Protocol Based on Reinforcement Learning

  • Kim, Tae-Wook (Department of Computer Engineering, Hanbat National University) ;
  • Hwang, Gyung-Ho (Department of Computer Engineering, Hanbat National University)
  • Received : 2021.01.04
  • Accepted : 2021.02.17
  • Published : 2021.03.31

Abstract

Reinforcement learning is an area of machine learning that studies how an intelligent agent takes actions in a given environment to maximize the cumulative reward. In this paper, we propose a new MAC protocol based on the Q-learning technique of reinforcement learning to improve the performance of the IEEE 802.11 wireless LAN CSMA/CA MAC protocol. Furthermore, the operation of each access point (AP) and station is proposed. The AP adjusts the value of the contention window (CW), which is the range for determining the backoff number of the station, according to the wireless traffic load. The station improves the performance by selecting an optimal backoff number with the lowest packet collision rate and the highest transmission success rate through Q-learning within the CW value transmitted from the AP. The result of the performance evaluation through computer simulations showed that the proposed scheme has a higher throughput than that of the existing CSMA/CA scheme.

Keywords

References

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