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 超可靠和低延迟通信教程：将领域知识集成到深度学习中


作为第五代和第六代（6G）移动通信网络的关键通信场景之一，超可靠低延迟通信（URLLC）将成为各种新兴关键任务应用开发的核心。最先进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。特别是，缺乏考虑延迟、可靠性、可用性、可扩展性和不确定性下决策的整体框架。在深度神经网络近期突破的推动下，深度学习算法被认为是在未来 6G 网络中开发 URLLC 支持技术的有前途的方法。本教程说明了如何将通信和网络的领域知识（模型、分析工具和优化框架）集成到 URLLC 的不同类型的深度学习算法中。我们首先提供 URLLC 的一些背景，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改进学习算法，我们重新审视了基于模型的分析工具和 URLLC 的跨层优化框架。接下来，我们研究了在 URLLC 中应用监督/无监督深度学习和深度强化学习的潜力，并总结了相关的开放问题。最后，我们提供仿真和实验结果来验证不同学习算法的有效性并讨论未来的方向。