In the future 6th generation networks, ultra-reliable and low-latency
communications (URLLC) will lay the foundation for emerging mission-critical
applications that have stringent requirements on end-to-end delay and
reliability. Existing works on URLLC are mainly based on theoretical models and
assumptions. The model-based solutions provide useful insights, but cannot be
directly implemented in practice. In this article, we first summarize how to
apply data-driven supervised deep learning and deep reinforcement learning in
URLLC, and discuss some open problems of these methods. To address these open
problems, we develop a multi-level architecture that enables device
intelligence, edge intelligence, and cloud intelligence for URLLC. The basic
idea is to merge theoretical models and real-world data in analyzing the
latency and reliability and training deep neural networks (DNNs). Deep transfer
learning is adopted in the architecture to fine-tune the pre-trained DNNs in
non-stationary networks. Further considering that the computing capacity at
each user and each mobile edge computing server is limited, federated learning
is applied to improve the learning efficiency. Finally, we provide some
experimental and simulation results and discuss some future directions.

本研究论文探讨了如何将数据驱动的深度学习和深度强化学习应用于未来 6G 网络中的 URLLC，构建了一种多层次架构，并采用迁移学习和联合学习等方法来提高网络延迟和可靠性

6G 网络中用于超高可靠性和低延迟通信的深度学习

Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G  Networks

The next frontier towards truly ubiquitous connectivity is the use of Low
Earth Orbit (LEO) small-satellite constellations to support 5G and Beyond-5G
(B5G) networks. Besides enhanced mobile broadband (eMBB) and massive
machine-type communications (mMTC), LEO constellations can support
ultra-reliable communications (URC) with relaxed latency requirements of a few
tens of milliseconds. Small-satellite impairments and the use of low orbits
pose major challenges to the design and performance of these networks, but also
open new innovation opportunities. This paper provides a comprehensive overview
of the physical and logical links, along with the essential architectural and
technological components that enable the full integration of LEO constellations
into 5G and B5G systems. Furthermore, we characterize and compare each physical
link category and explore novel techniques to maximize the achievable data
rates.

本文全面概述了在 5G 和 B5G 系统中将 LEO 卫星星座完全集成所必需的物理、逻辑连接和关键技术，并对每个物理链路类别进行了特征化和比较，并探讨了最大化可实现数据速率的新技术。

5G 与 Beyond-5G 通信的 LEO 小卫星星座

LEO Small-Satellite Constellations for 5G and Beyond-5G Communications

Learning the optimized solution as a function of environmental parameters is
effective in solving numerical optimization in real time for time-sensitive
applications. Existing works of learning to optimize train deep neural networks
(DNN) with labels, and the learnt solution are inaccurate, which cannot be
employed to ensure the stringent quality of service. In this paper, we propose
a framework to learn the latent function with unsupervised deep learning, where
the property that the optimal solution should satisfy is used as the
"supervision signal" implicitly. The framework is applicable to both functional
and variable optimization problems with constraints. We take a variable
optimization problem in ultra-reliable and low-latency communications as an
example, which demonstrates that the ultra-high reliability can be supported by
the DNN without supervision labels.

本文提出了一种基于无监督深度学习的学习优化的框架，用于解决数值优化问题，可同时应用于函数和变量优化问题，并以超高可靠性通信为例，证明了深度学习可实现无监督学习下的变量优化保障极高的可靠性通信。