As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLCs in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLCs. We first provide some background of URLLCs and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLCs. Following this, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLCs and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

中文翻译：

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6G中超可靠和低延迟通信的教程：将领域知识整合到深度学习中

作为第五代和第六代（6G）移动通信网络中的关键通信方案之一，超可靠和低延迟通信（URLLC）将是各种新兴任务关键型应用程序开发的中心。最新的移动通信系统无法满足URLLC的端到端延迟和整体可靠性要求。尤其是，缺少一种将延迟，可靠性，可用性，可伸缩性和不确定性下的决策考虑在内的整体框架。在深度神经网络的最新突破推动下，深度学习算法被认为是在未来的6G网络中开发URLLC的使能技术的有前途的方式。本教程说明了领域知识（模型，分析工具，通信和网络的最佳化和优化框架）可以集成到URLLC的各种深度学习算法中。我们首先提供URLLC的背景知识，并回顾有前途的6G网络架构和深度学习框架。为了更好地说明如何利用领域知识来改进学习算法，我们重新访问了基于模型的分析工具和URLLC的跨层优化框架。在此之后，我们研究了在URLLC中应用有监督/无监督的深度学习和深度强化学习的潜力，并总结了相关的开放性问题。最后，我们提供了仿真和实验结果，以验证不同学习算法的有效性，并讨论未来的发展方向。