Open Access
ARTICLE
Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission
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.Keywords
Cite This Article
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.