A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning
- URL: http://arxiv.org/abs/2009.06010v2
- Date: Wed, 20 Jan 2021 06:51:24 GMT
- Title: A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning
- Authors: Changyang She and Chengjian Sun and Zhouyou Gu and Yonghui Li and
Chenyang Yang and H. Vincent Poor and Branka Vucetic
- Abstract summary: Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks.
This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
- Score: 115.75967665222635
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As one of the key communication scenarios in the 5th and also the 6th
generation (6G) of mobile communication networks, ultra-reliable and
low-latency communications (URLLC) 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 URLLC. 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 URLLC 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 URLLC. We first
provide some background of URLLC 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 URLLC. Following that, we examine the
potential of applying supervised/unsupervised deep learning and deep
reinforcement learning in URLLC and summarize related open problems. Finally,
we provide simulation and experimental results to validate the effectiveness of
different learning algorithms and discuss future directions.
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