Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full cooperation between sensing and communication can result in significant performance gains, achieving optimal performance requires efficient designs of unified waveforms and beamformers for joint sensing and communication. Sophisticated statistical signal processing and multi-objective optimization techniques are necessary to balance the competing design requirements of joint sensing and communication tasks. As model-based approaches can be suboptimal or too complex, deep learning offers a powerful data-driven alternative, especially when optimal algorithms are unknown or impractical for real-time use. Unified waveform and beamformer design problems for ISAC fall into this category, where fundamental design trade-offs exist between sensing and communication performance metrics, and the underlying models may be inadequate or incomplete. This tutorial paper explores the application of artificial intelligence (AI) to enhance efficiency or reduce complexity in ISAC designs. We emphasize the integration benefits through AI-driven ISAC designs, prioritizing the development of unified waveforms, constellations, and beamforming strategies for both sensing and communication. To illustrate the practical potential of AI-driven ISAC, we present three case studies on waveform, beamforming, and constellation design, demonstrating how unsupervised learning and neural network-based optimization can effectively balance performance, complexity, and implementation constraints.

翻译：集成感知与通信（ISAC）有助于克服频谱资源有限和硬件成本高昂的挑战，从而提高能源与成本效益。虽然感知与通信的完全协同能带来显著的性能提升，但实现最优性能需要为联合感知与通信任务设计高效的统一波形与波束成形器。这需要采用复杂的统计信号处理与多目标优化技术，以平衡联合感知与通信任务中相互竞争的设计需求。由于基于模型的方法可能次优或过于复杂，深度学习提供了一种强大的数据驱动替代方案，尤其当最优算法未知或难以实时实现时。ISAC的统一波形与波束成形器设计问题正属于此类范畴，其中感知与通信性能指标之间存在固有的设计权衡，且底层模型可能不充分或不完整。本教程论文探讨了人工智能（AI）在提升ISAC设计效率或降低复杂度方面的应用。我们通过AI驱动的ISAC设计强调集成优势，优先开发适用于感知与通信的统一波形、星座图与波束成形策略。为阐明AI驱动ISAC的实际潜力，我们呈现了波形设计、波束成形与星座图设计三个案例研究，展示无监督学习与基于神经网络的优化方法如何有效平衡性能、复杂度与实现约束。