In this paper, we explore the potential of artificial intelligence (AI) to address challenges in terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We identify three key challenges for transceiver design: "hard to compute," "hard to model," and "hard to measure," and argue that AI can provide promising solutions. We propose three research roadmaps for AI algorithms tailored to THz UM-MIMO systems. The first, model-driven deep learning (DL), emphasizes leveraging domain knowledge and using AI to enhance bottleneck modules in established signal processing or optimization frameworks. We discuss four steps: algorithmic frameworks, basis algorithms, loss function design, and neural architecture design. The second roadmap presents channel station information (CSI) foundation models to unify transceiver module design by focusing on the wireless channel. We propose a compact foundation model to estimate wireless channel score functions, serving as a prior for designing transceiver modules. We outline four steps: general frameworks, conditioning, site-specific adaptation, and joint design of CSI models and model-driven DL. The third roadmap explores applying pre-trained large language models (LLMs) to THz UM-MIMO systems, with applications in estimation, optimization, searching, network management, and protocol understanding. Finally, we discuss open problems and future research directions.

翻译：本文探讨了人工智能在应对太赫兹超大规模多输入多输出系统挑战中的潜力。我们识别出收发机设计面临的三大核心挑战："计算困难"、"建模困难"与"测量困难"，并论证人工智能可为这些挑战提供创新解决方案。针对太赫兹超大规模MIMO系统，我们提出三条人工智能算法研究路径。第一条路径聚焦模型驱动深度学习，强调利用领域知识，通过人工智能增强现有信号处理或优化框架中的瓶颈模块。我们讨论了四个关键步骤：算法框架设计、基础算法构建、损失函数设计与神经架构设计。第二条路径提出信道状态信息基础模型，通过聚焦无线信道特性来统一收发机模块设计。我们提出一种紧凑型基础模型来估计无线信道评分函数，作为设计收发机模块的先验知识。该路径涵盖四个阶段：通用框架构建、条件化处理、站点自适应优化以及CSI模型与模型驱动深度学习的联合设计。第三条路径探索将预训练大语言模型应用于太赫兹超大规模MIMO系统，涉及估计、优化、搜索、网络管理和协议理解等应用场景。最后，我们讨论了该领域面临的开放性问题与未来研究方向。