arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.11045eess.SPcs.LGcs.NIstat.ML
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Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks

School of Electrical and Information Engineering, the University of Sydney(悉尼大学电气与信息工程学院);School of Electronics and Information Engineering, Beihang University(北京航空航天大学电子信息工程学院);School of Electrical Engineering and Computer Science, Peking University(北京大学电子工程与计算机科学学院)

Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou, Yonghui Li, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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