In this paper, we explore the potential of artificial intelligence (AI) to address the challenges posed by terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We begin by outlining the characteristics of THz UM-MIMO systems, and identify three primary challenges for the transceiver design: 'hard to compute', 'hard to model', and 'hard to measure'. We argue that AI can provide a promising solution to these challenges. We then propose two systematic research roadmaps for developing AI algorithms tailored for THz UM-MIMO systems. The first roadmap, called model-driven deep learning (DL), emphasizes the importance to leverage available domain knowledge and advocates for adopting AI only to enhance the bottleneck modules within an established signal processing or optimization framework. We discuss four essential steps to make it work, including algorithmic frameworks, basis algorithms, loss function design, and neural architecture design. Afterwards, we present a forward-looking vision through the second roadmap, i.e., physical layer foundation models. This approach seeks to unify the design of different transceiver modules by focusing on their common foundation, i.e., the wireless channel. We propose to train a single, compact foundation model to estimate the score function of wireless channels, which can serve as a versatile prior for designing a wide variety of transceiver modules. We will also guide the readers through four essential steps, including general frameworks, conditioning, site-specific adaptation, and the joint design of foundation models and model-driven DL.

翻译：本文探讨了人工智能（AI）在应对太赫兹超大规模多输入多输出（THz UM-MIMO）系统挑战方面的潜力。我们首先概述了THz UM-MIMO系统的特性，并识别出收发机设计面临的三大主要挑战："难以计算"、"难以建模"和"难以测量"。我们认为AI能为这些挑战提供有前景的解决方案。随后，我们提出了两条系统性的研究路线图，用于开发针对THz UM-MIMO系统的AI算法。第一条路线图称为模型驱动深度学习，强调利用现有领域知识的重要性，并主张仅在已建立的信号处理或优化框架内采用AI来增强瓶颈模块。我们讨论了使其有效的四个关键步骤，包括算法框架、基础算法、损失函数设计和神经架构设计。之后，我们通过第二条路线图——即物理层基础模型——提出了一个前瞻性愿景。该方法旨在通过聚焦于不同收发机模块的共同基础——即无线信道——来统一其设计。我们提出训练一个单一、紧凑的基础模型来估计无线信道的得分函数，该模型可作为设计多种收发机模块的通用先验。我们还将引导读者了解四个关键步骤，包括通用框架、条件化、站点特定适应以及基础模型与模型驱动深度学习的联合设计。