  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.
