Hybrid multiple-input multiple-output (MIMO) is an attractive technology for realizing extreme 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 a 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.

翻译：混合多输入多输出（MIMO）是实现未来无线通信所设想的可扩展且高能效的极致大规模MIMO系统的一项有吸引力的技术。然而，混合MIMO系统将其波束赋形部分在模拟域和数字域实现这一特性，使得其波束方向图的优化相比传统的全数字MIMO更具挑战性。因此，近年来，利用数据辅助的人工智能（AI）工具进行混合波束赋形设计引起了日益增长的关注。本文综述了利用数据来改进实时混合波束赋形设计的候选策略。我们讨论了架构约束，并总结了与混合波束赋形优化相关的核心挑战。接着，我们阐述了如何通过传统优化方法应对这些挑战，并识别了不同的AI辅助设计方法。这些方法大致可分为纯数据驱动的深度学习模型以及将AI与经典优化相结合的各种深度展开技术。我们对现有方法进行了系统性的比较研究，包括数值评估和定性度量。最后，我们展望了将AI融入混合MIMO系统相关的未来研究机遇。