Fast Deep Unfolded Hybrid Beamforming in Multiuser Large MIMO Systems
Nguyen, Nhan Thanh; Van Nguyen, Ly; Shlezinger, Nir; Swindlehurst, A. Lee; Juntti, Markku (2024-04-01)
IEEE
01.04.2024
N. T. Nguyen, L. Van Nguyen, N. Shlezinger, A. L. Swindlehurst and M. Juntti, "Fast Deep Unfolded Hybrid Beamforming in Multiuser Large MIMO Systems," 2023 57th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2023, pp. 486-490, doi: 10.1109/IEEECONF59524.2023.10476967
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409175894
https://urn.fi/URN:NBN:fi:oulu-202409175894
Tiivistelmä
Abstract
Hybrid beamforming (HBF) is a key enabler for massive multiple-input multiple-output (MIMO) systems thanks to its capability to maintain significant spatial multiplexing gains with low hardware cost and power consumption. However, HBF optimizations are often challenging due to the nonconvexity and highly coupled analog and digital beamformers. In this paper, we propose an efficient HBF method based on deep unfolding to maximize the sum rate of large multiuser MIMO systems. We first derive closed-form expressions for the gradients of the sum rate with respect to the analog and digital beamformers to develop a projected gradient ascent (PGA) framework. We then incorporate this framework with the deep unfolding technique in an unfolded PGA deep neural network, which efficiently outputs reliable hybrid beamformers with low complexity and fast ex-ecution thanks to the well-trained hyperparameters. Numerical results show that the proposed method converges much faster than the conventional PGA scheme and significantly outperforms the conventional PGA and the successive convex approximation counterparts.
Hybrid beamforming (HBF) is a key enabler for massive multiple-input multiple-output (MIMO) systems thanks to its capability to maintain significant spatial multiplexing gains with low hardware cost and power consumption. However, HBF optimizations are often challenging due to the nonconvexity and highly coupled analog and digital beamformers. In this paper, we propose an efficient HBF method based on deep unfolding to maximize the sum rate of large multiuser MIMO systems. We first derive closed-form expressions for the gradients of the sum rate with respect to the analog and digital beamformers to develop a projected gradient ascent (PGA) framework. We then incorporate this framework with the deep unfolding technique in an unfolded PGA deep neural network, which efficiently outputs reliable hybrid beamformers with low complexity and fast ex-ecution thanks to the well-trained hyperparameters. Numerical results show that the proposed method converges much faster than the conventional PGA scheme and significantly outperforms the conventional PGA and the successive convex approximation counterparts.
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