Power Management of IEEE 802.11 WLAN Using Reinforcement Learning 


Vol. 45,  No. 2, pp. 340-343, Feb.  2020
10.7840/kics.2020.45.2.340


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  Abstract

The WLAN(Wireless Local Area Network) technology based on IEEE 802.11 provides users with high performance but consumes a lot of power. So the IEEE 802.11 standard provides that the station can save the power consumption by switching the SLEEP state and AWAKE state. In this paper, we explain the problems that exist in conventional method and solve the problem by using the Q-learning model, a technology of reinforcement learning. Depending on the network traffic environment, the station determines the duration of the SLEEP state flexibly and minimizes unnecessary energy consumption. We explain how we implemented a series of IEEE 802.11 power management processes that were not defined in NS-3 and we show the energy consumption and frame delay and the simulation results.

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  Cite this article

[IEEE Style]

L. T. Hyun and S. H. Rhee, "Power Management of IEEE 802.11 WLAN Using Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 2, pp. 340-343, 2020. DOI: 10.7840/kics.2020.45.2.340.

[ACM Style]

Lim Tae Hyun and Seung Hyong Rhee. 2020. Power Management of IEEE 802.11 WLAN Using Reinforcement Learning. The Journal of Korean Institute of Communications and Information Sciences, 45, 2, (2020), 340-343. DOI: 10.7840/kics.2020.45.2.340.

[KICS Style]

Lim Tae Hyun and Seung Hyong Rhee, "Power Management of IEEE 802.11 WLAN Using Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 2, pp. 340-343, 2. 2020. (https://doi.org/10.7840/kics.2020.45.2.340)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880