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.

中文翻译：

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人工智能赋能的混合多输入/多输出波束成形：学习优化高吞吐量可扩展 MIMO


用于多输入/多输出 (MIMO) 通信的混合波束成形是一种极具吸引力的技术，可用于以可扩展且节能的方式实现未来无线通信所设想的超大规模 MIMO 系统。然而，与传统的全数字 MIMO 相比，混合 MIMO 系统以模拟方式实现部分波束成形，以数字方式实现部分波束成形，这使得其波束方向图的优化更具挑战性。因此，近年来人们对使用数据辅助人工智能 (AI) 工具进行混合波束成形设计的兴趣日益浓厚。本文回顾了利用数据改进实时混合波束成形设计的候选策略。我们讨论了架构限制并描述了与混合波束成形优化相关的核心挑战。然后，我们介绍如何通过传统优化来应对这些挑战，并确定不同的人工智能辅助设计方法。这些可以大致分为纯粹数据驱动的深度学习模型和将人工智能与经典优化相结合的不同形式的深度展开技术。我们对现有方法进行系统的比较研究，包括数值评估和定性测量。最后，我们提出了与将 AI 纳入混合 MIMO 系统相关的未来研究机会。