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Intelligent Interactive Beam Training for Millimeter Wave Communications

Zhang, J; Huang, Y; Wang, J; You, X; Masouros, C; (2020) Intelligent Interactive Beam Training for Millimeter Wave Communications. IEEE Transactions on Wireless Communications , 20 (3) pp. 2034-2048. 10.1109/TWC.2020.3038787. Green open access

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Abstract

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms.

Type: Article
Title: Intelligent Interactive Beam Training for Millimeter Wave Communications
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TWC.2020.3038787
Publisher version: https://doi.org/10.1109/TWC.2020.3038787
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Intelligent beam training, interactive learning design paradigm, environment sensing, beam image, deep reinforcement learning, millimeter wave communication
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10123243
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