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

• URL: http://arxiv.org/abs/2002.11045v1
• Date: Sat, 22 Feb 2020 14:38:11 GMT
• Title: Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
• Authors: Changyang She and Rui Dong and Zhouyou Gu and Zhanwei Hou and Yonghui Li and Wibowo Hardjawana and Chenyang Yang and Lingyang Song and Branka Vucetic
• Abstract summary: We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
• Score: 84.2155885234293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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