User-centric association in ultra-dense mmWave networks via deep reinforcement learning

Xue, Q., Sun, Y. , Wang, J., Feng, G., Yan, L. and Ma, S. (2021) User-centric association in ultra-dense mmWave networks via deep reinforcement learning. IEEE Communications Letters, 25(11), pp. 3594-3598. (doi: 10.1109/LCOMM.2021.3108013)

Abstract

For ultra-dense networks, user-centric architecture is regarded as a promising candidate to offer mobile users better quality of service. One of the main challenges of user-centric architecture is exploring efficient scheme for user association in the ultra-dense network. In this letter, we study dynamic user-centric association (UCA) problem for ultra-dense millimeter wave (mmWave) networks to provide reliable connectivity and high achievable data rate. We consider time-varying network environments and propose a deep Q-network based UCA scheme to find the optimal association policy based on the historical experience. Simulation results are presented to verify the performance gain of our proposed scheme.

• Item Type: Articles
• Additional Information: This work was supported in part by the NSFC under Grant 62001071, China Postdoctoral Science Foundation under Grant 2020M683291 and 2019TQ0270, Macao Young Scholars Program under Grant AM2021018, Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN201900617, Science and Technology Development Fund, Macau SAR (File no. 0036/2019/A1 and no. SKL-IOTSC2021-2023), and the Research Committee of University of Macau under Grant MYRG2018-00156-FST.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao and Feng, Professor Gang
• Authors: Xue, Q., Sun, Y., Wang, J., Feng, G., Yan, L., and Ma, S.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Communications Letters
• Publisher: IEEE
• ISSN: 1089-7798
• ISSN (Online): 1558-2558
• Published Online: 26 August 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Communications Letters 25(11): 3594-3598
• Publisher Policy: Reproduced in accordance with the publisher copyright policy