Beam management in ultra-dense mmwave network via federated reinforcement learning: an intelligent and secure approach

Xue, Q., Liu, Y.-J., Sun, Y. , Wang, J., Yan, L., Feng, G. and Ma, S. (2023) Beam management in ultra-dense mmwave network via federated reinforcement learning: an intelligent and secure approach. IEEE Transactions on Cognitive Communications and Networking, 9(1), pp. 185-197. (doi: 10.1109/TCCN.2022.3215527)

Abstract

Deploying ultra-dense networks that operate on millimeter wave (mmWave) band is a promising way to address the tremendous growth on mobile data traffic. However, one key challenge of ultra-dense mmWave network (UDmmN) is beam management due to the high propagation delay, limited beam coverage as well as numerous beams and users. In this paper, a novel systematic beam control scheme is presented to tackle the beam management problem which is difficult due to the non-convex objective function. We employ double deep Q-network (DDQN) under a federated learning (FL) framework to address the above optimization problem, and thereby fulfilling adaptive and intelligent beam management in UDmmN. In the proposed beam management scheme based on FL (BMFL), the non-raw-data aggregation can theoretically protect user privacy while reducing handoff cost. Moreover, we propose to adopt a data cleaning technique in the local model training for BMFL, with the aim to further strengthen the privacy protection of users while improving the learning convergence speed. Simulation results demonstrate the performance gain of our proposed scheme.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Mr Jian and Sun, Dr Yao and Feng, Professor Gang
• Authors: Xue, Q., Liu, Y.-J., Sun, Y., Wang, J., Yan, L., Feng, G., 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 Transactions on Cognitive Communications and Networking
• Publisher: IEEE
• ISSN: 2332-7731
• ISSN (Online): 2332-7731
• Published Online: 19 October 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Cognitive Communications and Networking 9(1): 185-197
• Publisher Policy: Reproduced in accordance with the publisher copyright policy