A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
arXiv:2009.06010
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.
arXiv commentThis work has been accepted by Proceedings of the IEEE
References in corpus (28)
- Continuous control with deep reinforcement learning
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Survey of Important Issues in UAV Communication Networks
- Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- Federated Learning with Non-IID Data
- Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management
- Deep Learning-Based Communication Over the Air
- Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research
- Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks
- Continuous Deep Q-Learning with Model-based Acceleration
- Learning Optimal Resource Allocations in Wireless Systems
- Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
- Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
- Heterogeneous Cellular Networks with Spatio-Temporal Traffic: Delay Analysis and Scheduling
- Stability Properties of Graph Neural Networks
- Hybrid Reward Architecture for Reinforcement Learning
- Ultrareliable and Low-Latency Communication Techniques for Tactile Internet Services
- A Bayesian Sampling Approach to Exploration in Reinforcement Learning
- Energy-Efficient Packet Scheduling with Finite Blocklength Codes: Convexity Analysis and Efficient Algorithms
- Blocklength-Limited Performance of Relaying under Quasi-Static Rayleigh Channels
- Extreme URLLC: Vision, Challenges, and Key Enablers
- Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
- Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning
- Saddlepoint Approximations for Short-Packet Wireless Communications
- Reward Shaping via Meta-Learning
- Taming the Tail of Maximal Information Age in Wireless Industrial Networks
- Model-Free Learning of Optimal Ergodic Policies in Wireless Systems