Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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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
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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.

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References

  1. "IEEE Standard for Information technology-Telecommunications and information exchange between systems Local and metropolitan area networks-Specific requirements- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications," in IEEE Std 802.11-2016, Dec. 2016. DOI: 10.1109/IEEESTD.2016.7786995.
  2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, A Bradford Book, 2018. DOI: 10.5555/3312046.
  3. H. V. Hasselt, A. Guez, and D. Silver, "Deep reinforcement learning with double Q-learning," in Proceedings of AAAI Conference on Artificial Intelligence, pp. 2094-2100, 2016. DOI: 10.5555/3016100.3016191.
  4. S. Galzarano, A. Liotta, and G. Fortino, "QL-MAC: A Q-learning based MAC for wireless sensor networks," in Proceedings of International Conference on Algorithms and Architectures for Parallel Processing, pp. 267-275, 2013. DOI: 10.1007/978-3-319-03889-6_31.
  5. Y. Chu, P. D. Mitchell, and D. Grace, "ALOHA and Q-learning based medium access control for wireless sensor networks," in Proceedings of International Symposium on Wireless Communication Systems (ISWCS), pp. 511-515, 2012. DOI: 10.1109/ISWCS.2012.6328420.
  6. M. Loganathan, T. Sabapathy, and M. Elshaikh, "Reinforcement learning based anti-collision algorithm for RFID systems," International Journal of Computing, pp. 155-168, 2019. DOI: 10.47839/ijc.18.2.1414.
  7. S. Dhakal and S. Shin, "Precise-optimal frame length based collision reduction schemes for frame slotted Aloha RFID systems," KSII Transactions on Internet and Information Systems, vol. 8, no. 1, pp. 165-182, 2014. DOI: 10.3837/tiis.2014.01.010.
  8. T. Ho and K. Chen, "Performance analysis of IEEE 802.11 CSMA/CA medium access control protocol," in Proceedings of PIMRC'96 - 7th International Symposium on Personal, Indoor, and Mobile Communications, pp. 407-411, 1996. DOI: 10.1109/PIMRC.1996.567426.