Artificial Intelligence-Empowered Hybrid Multiple-Input/Multiple-Output Beamforming: Learning to Optimize for High-Throughput Scalable MIMO

Nir Shlezinger, Mengyuan Ma, Ortal Lavi, Nhan Thanh Nguyen, Yonina C. Eldar, Markku Juntti

Research output: Contribution to journalArticlepeer-review

Abstract

Hybrid beamforming for multiple-input/multiple-output (MIMO) communications is an attractive technology for realizing extremely massive MIMO systems envisioned for future wireless communications in a scalable and power-efficient manner. However, the fact that hybrid MIMO systems implement part of their beamforming in analog and part in digital makes the optimization of their beampattern notably more challenging compared with conventional fully digital MIMO. Consequently, recent years have witnessed growing interest in using data-aided artificial intelligence (AI) tools for hybrid beamforming design. This article reviews candidate strategies to leverage data to improve real-time hybrid beamforming design. We discuss the architectural constraints and characterize the core challenges associated with hybrid beamforming optimization. We then present how these challenges are treated via conventional optimization, and identify different AI-aided design approaches. These can be roughly divided into purely data-driven deep learning models and different forms of deep unfolding techniques for combining AI with classical optimization. We provide a systematic comparative study between existing approaches, including both numerical evaluations and qualitative measures. We conclude by presenting future research opportunities associated with the incorporation of AI in hybrid MIMO systems.

Original languageEnglish
Pages (from-to)2-11
Number of pages10
JournalIEEE Vehicular Technology Magazine
Early online date20 May 2024
DOIs
StatePublished Online - 20 May 2024

Keywords

  • Antennas
  • Array signal processing
  • Finite element analysis
  • Iterative methods
  • Optimization
  • Phase shifters
  • Radio frequency

All Science Journal Classification (ASJC) codes

  • Automotive Engineering

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