Deep unfolding-enabled hybrid beamforming design for mmWave massive MIMO systems

Nguyen, Nhan; Ma, Mengyuan; Shlezinger, Nir; Eldar, Yonina C.; Swindlehurst, A. L.; Juntti, Markku (2023-05-05)
 
Avaa tiedosto
Lataukset: 

URL:
https://doi.org/10.1109/ICASSP49357.2023.10096658

Nguyen, Nhan
Ma, Mengyuan
Shlezinger, Nir
Eldar, Yonina C.
Swindlehurst, A. L.
Juntti, Markku
05.05.2023

N. Nguyen, M. Ma, N. Shlezinger, Y. C. Eldar, A. L. Swindlehurst and M. Juntti, "Deep Unfolding-Enabled Hybrid Beamforming Design for mmWave Massive MIMO Systems," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096658.

https://rightsstatements.org/vocab/InC/1.0/
© 2023 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.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/ICASSP49357.2023.10096658
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20230823103204
Tiivistelmä

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.

Kokoelmat