Open Access

ARTICLE

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

by Youngjun Kim¹, Taekook Kim², Hyungoo Choi¹, Jinwoo Park¹, Yeunwoong Kyung^3,*

1 School of Electrical Engineering, Korea University, Seoul, 02841, Korea
2 School of Computer Engineering, Pukyong National University, Busan, 48547, Korea
3 School of Computer Engineering, Hanshin University, Osan, 18101, Korea

* Corresponding Author: Yeunwoong Kyung. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 193-204. https://doi.org/10.32604/iasc.2023.032784

Received 29 May 2022; Accepted 01 July 2022; Issue published 29 September 2022

Abstract

Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there are few mobile stations (MSs) for the handover. Therefore, this paper proposes a reinforcement learning-based handover scheme with neighbor beacon frame transmission (MAN-BTS) to properly consider the use of N-BTS. The optimization equation is defined to maximize the expected reward to find the optimal policy and is solved using Q-learning. Simulation results show that the proposed scheme outperforms the comparison schemes in terms of the expected reward.

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Keywords

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