Reinforcement Learning Based Handoff for Millimeter Wave Heterogeneous Cellular Networks

Sun, Y. , Feng, G., Qin, S., Liang, Y.-C. and Yum, T.-S. P. (2018) Reinforcement Learning Based Handoff for Millimeter Wave Heterogeneous Cellular Networks. In: GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Singapore, 4-8 Dec 2017, ISBN 9781509050192 (doi: 10.1109/GLOCOM.2017.8254104)

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The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named LESH to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In LESH, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, LESH can significantly reduce the number of handoffs when compared with traditional handoff policies.

Item Type:Conference Proceedings
Additional Information:This work was supported by the National Science Foundation of China under Grant number 61631005 and 61471089, and the Fundamental Research Funds for the Central Universities under Grant number ZYGX2015Z005.
Glasgow Author(s) Enlighten ID:Liang, Professor Ying-Chang and Feng, Professor Gang and Sun, Dr Yao
Authors: Sun, Y., Feng, G., Qin, S., Liang, Y.-C., and Yum, T.-S. P.
College/School:College of Science and Engineering > School of Engineering
Copyright Holders:Copyright © 2017 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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