papers

Publications (44)

eess.SP2019

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…

cs.IT2025

Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

Yufeng Diao, Yichi Zhang, Changyang She +2

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in…

eess.SP2024

A Constrained Deep Reinforcement Learning Optimization for Reliable Network Slicing in a Blockchain-Secured Low-Latency Wireless Network

Xin Hao, Phee Lep Yeoh, Changyang She +3

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is…

cs.IT2022

A Scalable Graph Neural Network Decoder for Short Block Codes

Kou Tian, Chentao Yue, Changyang She +2

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

eess.SP2019

Prediction and Communication Co-design for Ultra-Reliable and Low-Latency Communications

Zhanwei Hou, Changyang She, Yonghui Li +2

Ultra-reliable and low-latency communications (URLLC) are considered as one of three new application scenarios in the fifth generation cellular networks. In this work, we aim to re…

cs.LG2023

Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks

Xin Hao, Phee Lep Yeoh, Changyang She +2

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile…

eess.SY2024

Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

Kui Wang, Kazuma Nonomura, Zongdian Li +6

Vehicle-road collaboration is a promising approach for enhancing the safety and efficiency of autonomous driving by extending the intelligence of onboard systems to smart roadside…

cs.RO2024

Intelligent Mode-switching Framework for Teleoperation

Burak Kizilkaya, Changyang She, Guodong Zhao +1

Teleoperation can be very difficult due to limited perception, high communication latency, and limited degrees of freedom (DoFs) at the operator side. Autonomous teleoperation is p…

cs.IT2016

Uplink Transmission Design with Massive Machine Type Devices in Tactile Internet

Changyang She, Chenyang Yang, Tony Q. S. Quek

In this work, we study how to design uplink transmission with massive machine type devices in tactile internet, where ultra-short delay and ultra-high reliability are required. To…

cs.RO2022

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

Zhen Meng, Changyang She, Guodong Zhao +1

The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with the help of Mixed Reality (MR) technologies; sti…

eess.SP2023

Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air

Yifan Gu, Changyang She, Zhi Quan +2

Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distribute…

cs.NI2025

Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation

Xin Hao, Changyang She, Phee Lep Yeoh +3

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios,…

eess.SY2024

Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach

Kui Wang, Changyang She, Zongdian Li +3

Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadsid…

cs.LG2024

GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach

Kou Tian, Chentao Yue, Changyang She +2

This paper presents a novel auto-encoder based end-to-end channel encoding and decoding. It integrates deep reinforcement learning (DRL) and graph neural networks (GNN) in code des…

cs.IT2021

A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana +3

The stringent requirements on reliability and processing delay in the fifth-generation (G) cellular networks introduce considerable challenges in the design of massive multiple-…

cs.IT2017

Energy-Efficient Resource Allocation for Ultra-reliable and Low-latency Communications

Chengjian Sun, Changyang She, Chenyang Yang

Ultra-reliable and low-latency communications (URLLC) is expected to be supported without compromising the resource usage efficiency. In this paper, we study how to maximize energy…

eess.SP2019

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…

eess.SP2020

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…

cs.NI2019

Computation Offloading for IoT in C-RAN: Optimization and Deep Learning

Chandan Pradhan, Ang Li, Changyang She +2

We consider computation offloading for Internet-of-things (IoT) applications in multiple-input-multiple-output (MIMO) cloud-radio-access-network (C-RAN). Due to the limited battery…

cs.IT2020

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

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.…

cs.NI2023

Task-Oriented Metaverse Design in the 6G Era

Zhen Meng, Changyang She, Guodong Zhao +4

As an emerging concept, the Metaverse has the potential to revolutionize the social interaction in the post-pandemic era by establishing a digital world for online education, remot…

cs.IT2018

Improving Network Availability of Ultra-Reliable and Low-Latency Communications with Multi-Connectivity

Changyang She, Zhengchuan Chen, Chenyang Yang +3

Ultra-reliable and low-latency communications (URLLC) have stringent requirements on quality-of-service and network availability. Due to path loss and shadowing, it is very challen…

cs.IT2017

Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access Networks

Changyang She, Chenyang Yang, Tony Q. S. Quek

In this paper, we propose a framework for cross-layer optimization to ensure ultra-high reliability and ultra-low latency in radio access networks, where both transmission delay an…

cs.IT2018

Joint Uplink and Downlink Resource Configuration for Ultra-reliable and Low-latency Communications

Changyang She, Chenyang Yang, Tony Q. S. Quek

Supporting ultra-reliable and low-latency communications (URLLC) is one of the major goals for the fifth-generation cellular networks. Since spectrum usage efficiency is always a c…

cs.RO2023

Task-Oriented Prediction and Communication Co-Design for Haptic Communications

Burak Kizilkaya, Changyang She, Guodong Zhao +1

Prediction has recently been considered as a promising approach to meet low-latency and high-reliability requirements in long-distance haptic communications. However, most of the e…

cs.RO2023

Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse

Zhen Meng, Kan Chen, Yufeng Diao +4

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the M…

cs.IT2016

Cross-layer Transmission Design for Tactile Internet

Changyang She, Chenyang Yang, Tony Q. S. Quek

To ensure the low end-to-end (E2E) delay for tactile internet, short frame structures will be used in 5G systems. As such, transmission errors with finite blocklength channel codes…

cs.IT2023

Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications

Xin Hao, Phee Lep Yeoh, Yuhong Liu +3

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdr…

cs.NI2019

Cross-layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

Changyang She, Yifan Duan, Guodong Zhao +3

In this work, we propose a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for Mission-Critical Internet-of-Things (MC-IoT)…

cs.RO2024

Real-Time Interactions Between Human Controllers and Remote Devices in Metaverse

Kan Chen, Zhen Meng, Xiangmin Xu +2

Supporting real-time interactions between human controllers and remote devices remains a challenging goal in the Metaverse due to the stringent requirements on computing workload,…

cs.NI2020

Energy-Aware Offloading in Time-Sensitive Networks with Mobile Edge Computing

Mingxiong Zhao, Jun-Jie Yu, Wen-Tao Li +5

Mobile Edge Computing (MEC) enables rich services in close proximity to the end users to provide high quality of experience (QoE) and contributes to energy conservation compared wi…

eess.SP2024

Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype

Haiyao Yu, Changyang She, Yunkai Hu +4

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues o…

cs.IT2016

Energy Efficient Design for Tactile Internet

Changyang She, Chenyang Yang

Ensuring the ultra-low end-to-end latency and ultrahigh reliability required by tactile internet is challenging. This is especially true when the stringent Quality-of-Service (QoS)…

cs.LG2021

Machine Learning for Massive Industrial Internet of Things

Hui Zhou, Changyang She, Yansha Deng +2

Industrial Internet of Things (IIoT) revolutionizes the future manufacturing facilities by integrating the Internet of Things technologies into industrial settings. With the deploy…

eess.SP2021

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…

cs.IT2019

Energy Efficient Resource Allocation for Hybrid Services with Future Channel Gains

Changyang She, Chenyang Yang

In this paper, we propose a framework to maximize energy efficiency (EE) of a system supporting real-time (RT) and non-real-time services by exploiting future average channel gains…

cs.LG2025

Graph Neural Networks for Resource Allocation in Interference-limited Multi-Channel Wireless Networks with QoS Constraints

Lili Chen, Changyang She, Jingge Zhu +1

Meeting minimum data rate constraints is a significant challenge in wireless communication systems, particularly as network complexity grows. Traditional deep learning approaches o…

eess.SP2022

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

Yuhong Liu, Changyang She, Yi Zhong +3

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. To obtain time…

eess.SP2020

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…

cs.IT2025

Radio Map-Enabled 3D Trajectory and Communication Optimization for Low-Altitude Air-Ground Cooperation

Menghao Hu, Tong Zhang, Shuai Wang +4

Low-altitude economy includes the application of unmanned aerial vehicles (UAVs) serving ground robots. This paper investigates the 3-dimensional (3D) trajectory and communication…

cs.CV2024

Timeliness-Fidelity Tradeoff in 3D Scene Representations

Xiangmin Xu, Zhen Meng, Yichi Zhang +2

Real-time three-dimensional (3D) scene representations serve as one of the building blocks that bolster various innovative applications, e.g., digital manufacturing, Virtual/Augmen…

cs.IT2024

The Guesswork of Ordered Statistics Decoding: Guesswork Complexity and Decoder Design

Chentao Yue, Changyang She, Branka Vucetic +1

This paper investigates guesswork over ordered statistics and formulates the achievable guesswork complexity of ordered statistics decoding (OSD) in binary additive white Gaussian…

eess.SP2021

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…

cs.LG2025

Graph Neural Networks for Resource Allocation in Multi-Channel Wireless Networks

Lili Chen, Changyang She, Jingge Zhu +1

As the number of mobile devices continues to grow, interference has become a major bottleneck in improving data rates in wireless networks. Efficient joint channel and power alloca…