@inproceedings{8e24ab3280204234805619df583668ef,
title = "Deep Unfolding-Enabled Hybrid Beamforming Design for mmWave Massive MIMO Systems",
abstract = "Hybrid beamforming (HBF) is a key enabler for millimeter-wave (mmWave) communications systems, but HBF optimizations are often non-convex and of large dimension. In this paper, we propose an efficient deep unfolding-based HBF scheme, referred to as ManNet-HBF, that approximately maximizes the system spectral efficiency (SE). It first factorizes the optimal digital beamformer into analog and digital terms, and then reformulates the resultant matrix factorization problem as an equivalent maximum-likelihood problem, whose analog beamforming solution is vectorized and estimated efficiently with ManNet, a lightweight deep neural network. Numerical results verify that the proposed ManNet-HBF approach has near-optimal performance comparable to or better than conventional model-based counterparts, with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, it attains 98.62\% the SE of the Riemannian manifold scheme but 13250 times faster.",
keywords = "AI, deep learning, deep unfolding, hybrid beamforming, massive MIMO, mmWave",
author = "Nhan Nguyen and Mengyuan Ma and Nir Shlezinger and Eldar, \{Yonina C.\} and Swindlehurst, \{A. L.\} and Markku Juntti",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/ICASSP49357.2023.10096658",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing",
}