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

arXiv: https://arxiv.org/abs/2002.11045 · HTML: https://mlsanity.com/paper/2002.11045/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services](https://mlsanity.com/paper/2208.05438/) (2022)
- [AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends](https://mlsanity.com/paper/2208.10921/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [Federated Learning for Metaverse: A Survey](https://mlsanity.com/paper/2303.17987/) (2023)
- [Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT](https://mlsanity.com/paper/2107.00481/) (2021)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT](https://mlsanity.com/paper/2401.06135/) (2023)
- [A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy](https://mlsanity.com/paper/2602.13207/) (2026)

## Abstract (arXiv)

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.