Search Results for author: Changyang She

Found 11 papers, 0 papers with code

A Scalable Graph Neural Network Decoder for Short Block Codes

no code implementations13 Nov 2022 Kou Tian, Chentao Yue, Changyang She, Yonghui Li, Branka Vucetic

In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN).

Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse

no code implementations31 Jul 2022 Zhen Meng, Changyang She, Guodong Zhao, Daniele De Martini

This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse.

Mixed Reality

Distributed Graph Neural Networks for Optimizing Wireless Networks: Message Passing Over-the-Air

no code implementations18 Jul 2022 Yifan Gu, Changyang She, Zhi Quan, Chen Qiu, Xiaodong Xu

In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links.

Graph Embedding

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

no code implementations11 Jul 2022 Yuhong Liu, Changyang She, Yi Zhong, Wibowo Hardjawana, Fu-Chun Zheng, Branka Vucetic

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks.

Machine Learning for Massive Industrial Internet of Things

no code implementations10 Mar 2021 Hui Zhou, Changyang She, Yansha Deng, Mischa Dohler, Arumugam Nallanathan

With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements.

BIG-bench Machine Learning

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

no code implementations17 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 Scheduling

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

no code implementations13 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

Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints

no code implementations30 May 2020 Chengjian Sun, Changyang She, Chenyang Yang

Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems.

Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G

no code implementations29 Mar 2020 Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

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 network conditions.

Quantization Transfer Learning

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

no code implementations22 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

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin

no code implementations30 Jun 2019 Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.

Association Edge-computing +1

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