About Me

Dr Wanchun Liu is a Senior Lecturer and an ARC Discovery Early Career Researcher Award (DECRA) Fellow at the Faculty of Engineering, the University of Sydney.

Her research lies at the intersection of wireless communications, control, and intelligent systems, advancing the theoretical and practical foundations of next-generation cyber-physical and cyber-physical-human systems.

Her work focuses on developing fundamental models and architectures that unify communication, control, and computation, enabling wireless systems that are intelligent, energy-efficient, and capable of real-time adaptation. She applies these principles to domains including networked robotics, industrial Internet of Things (IIoT), and human–machine collaboration, aiming to create wireless networks that seamlessly integrate physical and human dynamics.

She has pioneered foundational advances across three transformative areas:

1) Splitting receiver design (paper 1, paper 2, paper 3, paper 4), advancing high-throughput and ultra-reliable communications for future networks.

2) Over-the-air computation (paper 1, paper 2, paper 3, paper 4), enabling large-scale data fusion and distributed intelligence across wireless sensors and robotic systems.

3) Wireless networked control (paper 1, paper 2, paper 3), establishing the fundamental stability conditions of control systems operating over practical wireless channels.

Recent Papers on machine learning-based communications and control:

• J. Chen and W. Liu, "Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach", in IEEE Transactions on Wireless Communications, 2025 (arXiv)

• G. Pang, W. Liu, et al., "Communication-Control Codesign for Large-Scale Wireless Networked Control Systems," in IEEE Journal on Selected Areas in Communications, 2025 (arXiv)

• G. Pang, K. Huang, D. E. Quevedo, B. Vucetic, Y. Li, W. Liu "Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control", in IEEE Transactions on Cybernetics, 2025 (arXiv)

• K. Wang, W. Liu, et al., "Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks", submitted, 2024 (arXiv)

• J. Chen, W. Liu, et al., "Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach", in IEEE Communications Letters 2024 (arXiv)

• J. Chen, W. Liu, et al., "Structure-Enhanced DRL for Optimal Transmission Scheduling", in IEEE Transactions on Wireless Communications, 2023 (arXiv)

• Z. Zhao, W. Liu, et al., "Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach", in IEEE Internet of Things Journal, 2023 (arXiv, GitHub)

• K. Wang, W. Liu, T. Lim, "Deep Learning for Radio Resource Allocation under DoS Attack", in IEEE Transactions on Machine Learning in Communications and Networking, 2024

• A. Leong, D. Quevedo, and W. Liu "Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes", in IEEE Transactions on Automatic Control, 2023 (arXiv)

• G. Pang, W. Liu, et al., "DRL-based Resource Allocation in Remote State Estimation", in IEEE Transactions on Wireless Communications (arXiv)

Email: wanchun.liu AT sydney.edu.au

Address: J13, The University of Sydney, Darlington, NSW 2008, Australia