Multi-User Position based on Trajectories-Aware Handover Strategy for Base Station Selection with Multi-Agent Learning

Mollel, M. S. , Kaijage, S. F., Kisangiri, M., Imran, M. and Abbasi, Q. H. (2020) Multi-User Position based on Trajectories-Aware Handover Strategy for Base Station Selection with Multi-Agent Learning. In: IEEE International Conference on Communications, Dublin, Ireland, 07-11 Jun 2020, ISBN 9781728174402 (doi: 10.1109/ICCWorkshops49005.2020.9145184)

Abstract

This paper presents the optimal Base Station(BS) selection method for proactive decision handover(HO) in Millimeter-wave(mm-wave) wireless communication. Mm-wave spectrum suffers significantly from the high path-loss and blockage caused by either controlled or uncontrolled sources. While the primary purpose of utilizing mm-wave is to achieve a high data rate, the presence of obstacle degrade the overall system performance since the connection link between User(UE) and serving BS being intermittent. The repercussion of the sporadic link is an increased number of HO. To increase throughput, proactive HO and minimize unnecessary HO are considered as the solution, and this paper presents a solution based on Reinforcement Learning(RL) framework. The framework learns from multi UE trajectories, and smart-agent learns simultaneously using Multi-Agent RL(MARL) and mapping each trajectory's feature and respect Q-value in smart agent constructed from Artificial Neural Network(ANN). The numerical results show that the intelligent, learned agent minimizes the number of HO and also outperform heuristic HO strategy in terms of throughput.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Mollel, Dr Michael and Abbasi, Professor Qammer
• Authors: Mollel, M. S., Kaijage, S. F., Kisangiri, M., Imran, M., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2474-9133
• ISBN: 9781728174402
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in 2020 IEEE International Conference on Communications Workshops (ICC Workshops)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy