Artificial Intelligence-Empowered Hybrid Multiple-Input/Multiple-Output Beamforming: Learning to Optimize for High-Throughput Scalable MIMO
Shlezinger, Nir; Ma, Mengyuan; Lavi, Ortal; Nguyen, Nhan Thanh; Eldar, Yonina C.; Juntti, Markku (2024-05-20)
IEEE
20.05.2024
N. Shlezinger, M. Ma, O. Lavi, N. T. Nguyen, Y. C. Eldar and M. Juntti, "Artificial Intelligence-Empowered Hybrid Multiple-input/multiple-output Beamforming: Learning to Optimize for High-Throughput Scalable MIMO," in IEEE Vehicular Technology Magazine, vol. 19, no. 3, pp. 58-67, Sept. 2024, doi: 10.1109/MVT.2024.3396927.
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© 2024 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.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409165877
https://urn.fi/URN:NBN:fi:oulu-202409165877
Tiivistelmä
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.
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.
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