Xiaoxia Xu

Xiaoxia Xu

Postdoctoral researcher on 6G advanced antennas & multiple access techniques, AI-native 6G networks, and large model over 6G

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About

I am a Postdoctoral Researcher at Queen Mary University of London, working on advanced 6G communication techniques and AI-native wireless communications, under the supervision of Prof. Arumugam Nallanathan and Prof. Yuanwei Liu.

My research lies at the intersection of advanced wireless communication theory and large AI models, with a particular focus on Pinching-Antenna Systems (PASS), AI-native 6G Networks, Optimization Theory, and Mobile Edge Intelligence.

I received my Ph.D. in Electronic Information School from Wuhan University in 2023, and my B.Eng. in Remote Sensing Information Engineering School from Wuhan University in 2017. From 2021 to 2022, I was a Visiting Student at the School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London. I joined QMUL as a Postdoctoral Researcher in 2024 (remotely since September 2023).

Research Highlights

  • Pinching-Antenna Systems (PASS), Reconfigurable Intelligent Surface (RIS)
    Developing new antenna paradigms for 6G with controllable radiation and flexible beamforming.

  • AI-Native Wireless Networks
    Integrating large AI models and model-based learning into wireless systems for learning-driven communication and resource optimization.

  • Mobile Edge Generation (MEG)
    Enabling efficient deployment and inference of large models at the network edge.

  • Next-Generation Multiple Access (NGMA)
    Designing scalable and intelligent multiple access schemes beyond conventional NOMA.

Education

  • Ph.D., Electronic Information School, Wuhan University, 2023. (Supervisor: Prof. Jiang Hao and Qimei Chen)
  • Visiting Student, EECS, Queen Mary University of London, 2021–2022. (Supervisor: Prof. Yuanwei Liu)
  • B.Eng., Remote Sensing Information Engineering, Wuhan University, 2017

Recent Selected Publications

👉 Full list available at Publications

Pinching-Antenna Systems (PASS)

  • X. Xu, X. Mu, Y. Liu, and A. Nallanathan. Multi-Mode Pinching-Antenna Systems: Mode Selection or Mode Combining? arXiv preprint, 2026. [ArXiv] [Code]

    Multi-Mode PASS EM Coupling Protocol PSO-KPBF Mode-Domain Multiplexing

  • X. Xu, X. Mu, Y. Liu, and A. Nallanathan. Multi-Mode Pinching Antenna Systems Enabled Multi-User Communications. arXiv preprint, 2026. [ArXiv]

    Multi-Mode PASS Physic Model Channel Orthogonization Mode-Domain Multiplexing PSO-ZF

  • X. Xu, X. Mu, Z. Wang, Y. Liu, and A. Nallanathan. Pinching-antenna systems (PASS): Power radiation model and optimal beamforming design. IEEE Transactions on Communications, 2025. [Paper] [Code]

    PASS Discrete PA Position Branch and Bound (BnB) Many-to-Many Matching Globally Optimal Solution

  • X. Xu, X. Mu, Y. Liu, and A. Nallanathan. Joint transmit and pinching beamforming for pinching antenna systems (PASS): Optimization-based or learning-based? IEEE Transactions on Wireless Communications, 2026. [Paper] [Code]

    PASS Pinching Beamforming Continuous PA Position Model-based Learning Large Model KKT-Guided Transformer

AI-Native Wireless Networks and Edge Intelligence

  • X. Xu, X. Mu, Y. Liu, H. Xing, Y. Liu, and A. Nallanathan. Generative artificial intelligence for mobile communications: A diffusion model perspective. IEEE Communications Magazine, vol. 63, no. 7, pp. 98-105, 2024. [Paper] [Code]

    Diffusion Model Large AI Model Channel Estimation

  • X. Xu, X. Mu, Y. Liu, Y. H. Kim, and A. Nallanathan. Large Model at Edge: An Optimal Mobile Edge Generation (MEG) Design. IEEE Transactions on Wireless Communications, 2025. [Paper] [Code]

    Mobile Edge Generation Large AI Model Generation Splitting Energy Consumption Latency Global Optimization

  • X. Xu, Y. Liu, X. Mu, H. Xing, and A. Nallanathan. Accelerating mobile edge generation (MEG) by constrained learning. IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 3, pp. 1854-1869, 2025. [Paper] [HuggingFace][Dataset]

    Mobile Edge Generation Denoising Step Compression Feature Compression Low Latency Constrained Reinforcement Learning

  • X. Xu, R. Zhong, X. Mu, Y. Liu, and K. Huang. Mobile edge generation-enabled digital twin: Architecture design and research opportunities. IEEE Communications Magazine, vol. 63, no. 4, pp. 32-39, 2024. [Paper] [Code]

    Mobile Edge Generation Low Latency Distributed/Collaborative Edge-Device Generation JSCC

News

  • 2026: Our tutorial paper on PASS is selected as IEEE TCOM Popular Article
  • 2025: Our GAI paper is selected as IEEE COMMAG Popular Article
  • 2025: Our MEG paper is selected as IEEE TCCN Popular Article
  • 2024: I joined Queen Mary University of London as a Postdoctoral Researcher.
  • 2023: I received my PhD Degree from Wuhan University.
  • 2023: Our AI for NOMA paper is selected as IEEE WCM Popular Article

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