Deep Unfolding-Enabled Hybrid Beamforming Design for mmWave Massive MIMO Systems

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

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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