Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

no code implementations • 17 Sep 2020 • Zhouyou Gu, Changyang She, Wibowo Hardjawana, Simon Lumb, David McKechnie, Todd Essery, Branka Vucetic

Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ~ 50% packet losses).

Reinforcement Learning (RL) Scheduling

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

no code implementations • 13 Sep 2020 • Changyang She, Chengjian Sun, Zhouyou Gu, Yonghui Li, Chenyang Yang, H. Vincent Poor, Branka Vucetic

As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.

Decision Making Decision Making Under Uncertainty

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

no code implementations • 22 Feb 2020 • Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou, Yonghui Li, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic

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.

Edge-computing Federated Learning +1