University of Oulu

M. Fozi, A. R. Sharafat and M. Bennis, "Fast MIMO Beamforming via Deep Reinforcement Learning for High Mobility mmWave Connectivity," in IEEE Journal on Selected Areas in Communications, vol. 40, no. 1, pp. 127-142, Jan. 2022, doi: 10.1109/JSAC.2021.3126056

Fast MIMO beamforming via deep reinforcement learning for high mobility mmWave connectivity

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Author: Fozi, Mahdi1; Sharafat, Ahmad R.1; Bennis, Mehdi2,3
Organizations: 1Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14117-13116, Iran
2Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
3Academy of Finland, 00531 Helsinki, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-31


Future 5G/6G wireless networks will be increasingly using millimeter waves (mmWaves), where fast and efficient beamforming is vital for providing continuous service to highly mobile devices in the presence of interference and signal attenuation, manifested by blockage. In this paper, we propose a novel and efficient method for mmWave beamforming in massive multiple-input multiple-output (MIMO) systems to achieve the aforementioned goals with low complexity in such scenarios. In doing so, we utilize deep reinforcement learning (DRL) to maximize the network’s energy efficiency subject to the quality of service (QoS) constraint for each user equipment (UE) and obtain its hybrid beamforming matrices. In doing so, we assume each UE is simultaneously associated with multiple access points (APs), i.e., simultaneous beamforming to/from multiple APs (coordinated multipoints) is needed for each UE. We also propose a low-complexity training algorithm, based on approximate message passing, which is well suited for the network edge. Besides, we develop a distributed scheme to reduce communications overhead via federated DRL. Extensive simulations show significant performance improvement over existing methods.

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Series: IEEE journal on selected areas in communications
ISSN: 0733-8716
ISSN-E: 1558-0008
ISSN-L: 0733-8716
Volume: 40
Issue: 1
Pages: 127 - 142
DOI: 10.1109/jsac.2021.3126056
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Academy of Finland 6G Flagship under Grant 318927.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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