A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning

By Changyang She, Chengjian Sun, Zhouyou Gu, Yonghui Li, Chenyang Yang, H. Vincent Poor, Branka Vucetic. Proceedings of the IEEE, 2021

335 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1ada57bab24abb5b0bb2bbd95765de4e0959c911

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “The domain knowledge (e.g., optimization models and theoretical tools) was further incorporated into the DL framework for optimizing ultra-reliable and low-latency communication networks [26].”
Artificial Intelligence for the Metaverse: A Survey (2022, background): “In this context, ultra-reliable and lowlatency communications (uRLLC) represent the foundation to enable the development of emerging mission-critical applications [108].”
6G Internet of Things: A Comprehensive Survey (2021, methodology): “Enabled by the fundamental technologies as described in the previous section, 6G is envisioned to realize new applications for IoT.”
Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory (2022, background): “In [153], the authors discussed how the domain knowledge, i.”