Z
Zhouyou Gu
Researcher at University of Sydney
Publications - 8
Citations - 389
Zhouyou Gu is an academic researcher from University of Sydney. The author has contributed to research in topics: Reinforcement learning & Deep learning. The author has an hindex of 5, co-authored 8 publications receiving 117 citations.
Papers
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Journal ArticleDOI
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning
Changyang She,Chengjian Sun,Zhouyou Gu,Yonghui Li,Chenyang Yang,H. Vincent Poor,Branka Vucetic +6 more
TL;DR: In this paper, the authors discuss the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in ultrareliable and low-latency communications (URLLCs) in future 6G networks.
Journal ArticleDOI
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
Changyang She,Rui Dong,Zhouyou Gu,Zhanwei Hou,Yonghui Li,Wibowo Hardjawana,Chenyang Yang,Lingyang Song,Branka Vucetic +8 more
TL;DR: In this paper, a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC is proposed, where deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks.
Posted Content
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
Changyang She,Rui Dong,Zhouyou Gu,Zhanwei Hou,Yonghui Li,Wibowo Hardjawana,Chenyang Yang,Lingyang Song,Branka Vucetic +8 more
TL;DR: A multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC is developed and deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks.
Posted Content
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Zhouyou Gu,Changyang She,Wibowo Hardjawana,Simon Lumb,David McKechnie,Todd Essery,Branka Vucetic +6 more
TL;DR: A knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic and an architecture for online training and inference, where K-DDPG initializes the Scheduler off-line and then fine-tunes the scheduler online to handle the mismatch between off- line simulations and non-stationary real-world systems.
Journal ArticleDOI
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Zhouyou Gu,Changyang She,Wibowo Hardjawana,Simon Lumb,David McKechnie,Todd Essery,Branka Vucetic +6 more
TL;DR: In this article, a knowledge-assisted deep reinforcement learning (DRL) algorithm was proposed to design wireless schedulers in the 5G cellular networks with time-sensitive traffic, where the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions.