19 citations · 41 across the 12 of their papers we have counts for
6 papers · 1 filter
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Zhouyou Gu, Changyang She, Wibowo Hardjawana +4
In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-se…
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
Changyang She, Chengjian Sun, Zhouyou Gu +4
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 b…
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G
Rui Dong, Changyang She, Wibowo Hardjawana +2
To accommodate diverse Quality-of-Service (QoS) requirements in the 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying n…
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
Changyang She, Rui Dong, Zhouyou Gu +6
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…
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
Rui Dong, Changyang She, Wibowo Hardjawana +2
In this work, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normal…
Towards Ultra-Reliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues
Daquan Feng, Changyang She, Kai Ying +5
Ultra-reliable low-latency communications (URLLC) has been considered as one of the three new application scenarios in the \emph{5th Generation} (5G) \emph {New Radio} (NR), where…