University of Oulu

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.

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

Saved in:
Author: Nguyen, Nhan1; Ma, Mengyuan1; Shlezinger, Nir2;
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
2School of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel
3Faculty of Math and CS, Weizmann Institute of Science, Rehovot, Israel
4Department of EECS, University of California, Irvine, CA, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link:
Language: English
Published: , 2023
Publish Date: 2023-08-23


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.

see all

ISBN: 978-1-7281-6327-7
ISBN Print: 978-1-7281-6328-4
Pages: 1 - 5
DOI: 10.1109/ICASSP49357.2023.10096658
Host publication: Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference: International Conference on Acoustics, Speech and Signal Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Copyright information: © 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.