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Undergraduate modules

  1. COMP123 Data Communications
  2. COMP411 Digital Image and Video Processing
  3. COMP405 Mobile Computing and Wireless Networks
  4. COMP311 Multimedia Application Development
  5. COMP490 Final Year Project

Postgraduate modules

  1. Master Dissertation
  2. PhD Thesis


Yue Liu received a B.Sc. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 2010 and a Ph.D. degree in electronic engineering from Queen Mary University of London, London, U.K., in 2014. In the same year, she joined MPI-QMUL Information Systems Research Centre, Macao SAR, China as a researcher and then she is working as a lecturer in the Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China, since 2015.

Her research interests include 6G network, Radio Resource Management(RRM), Integrated Sensing and Communication(ISAC), RF-signal sensing, Reinforcement Learning, Deep Learning, AI-driven cellular network.

  • Current Employer/Organization

    Macao Polytechnic University - Faculty of Applied Sciences

  • Current Position

    Lecturer/Assistant Programme Coordinator

    Macao Polytechnic University

  • Subjects Taught

    Undergraduate modules

    1. COMP123 Data Communications
    2. COMP411 Digital Image and Video Processing
    3. COMP405 Mobile Computing and Wireless Networks
    4. COMP311 Multimedia Application Development
    5. COMP490 Final Year Project

    Postgraduate modules

    1. Master Dissertation
    2. PhD Thesis


  • Education

    PhD in Electronic Engineering, Queen Mary University of London

    BSc in Telecommunications, Beijing University of Posts and Telecommunications

  • Research Interests

    1. 6G network
    2. Radio Resource Management(RRM)
    3. Integrated Sensing and Communication(ISAC)
    4. RF-signal sensing
    5. Reinforcement Learning
    6. Deep Learning

  • Publications

    Journal paper

    1. C. Liu, Y. Hao, Y. Liu*, X. Zhang and X. Wang and Y. Liu, "LWiHS: A Lightweight WiFi-enabled Human Sensing Using Feature Fusion Strategy," in IEEE Transactions on Instrumentation & Measurement. (JCR, Q1, 2023 JIF 5.6)
    2. Z. Wang, M. Luo, X. Xie, Y. Sun, X. Tian, Z. Chen, J. Xie, Q. Gao, T. Tong, Y. Liu and T. Tan, “MMSC-Net:Unified Multi-Scale Spatial Channel Network for End-End Industrial Defect Detection”, IEEE Access 2024 (JCR Q2, 2023 JIF 3.4) 
    3. S. Lv, M. Li, C. Liu, X. Xu, R. Meng, Y. Liu and Y. Liu, "Short-Packet Transmission in NOMA-ISAC Systems", in IEEE Transactions on Cognitive Communications and Networking ( JCR Q1, 2023 JIF 7.4)
    4. X. Xu, X. Mu, Y. Liu, H. Xing, Y. Liu, A. Nallanathan, "Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective," in IEEE Communications Magazine, doi: 10.1109/MCOM.001.2400284. (JCR Q1, 2022 JIF 12.7)
    5. Ji Wang, Jiayi Sun, Fang Wei, Zhao Chen, Y. Liu and Yuanwei Liu, “Deep Reinforcement Learning for Near-Field Wideband Beamforming in STAR-RIS Networks”, Front Inform Technol. Electron. Eng. 25, 1651–1663 (2024). https://doi.org/10.1631/FITEE.2400364 (JCR Q2, 2023 JIF 2.7)
    6. Y. Yu, H. Li, D. Wong, Y. Ma and Y. Liu*, "POMA-C: A Framework for Solving the Problem of Precise Anesthesia Control Under Incomplete Observation Environment in Low-Income Areas," in IEEE Access, vol. 13, pp. 4098-4116, 2025, doi: 10.1109/ACCESS.2024.3524262. (JCR Q2, 2023 JIF 3.4)
    7. Y. Zou, Y. Liu, X. Mu, X. Zhang, Y. Liu* and C. Yuen, "Machine Learning in RIS-assisted NOMA IoT Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3245288. (JCR, Q1, 2022 JIF 10.6)
    8. Y. Wang, Y. Yu, Y. Liu*, Y. Ma and P. Pang, "Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach", JIMR Mental Health 2023, doi: 10.2196/49894 (JCR Q1, 2022 JIF 5.2)
    9. Y. Yu, Y. Liu, D. Wong, H. Li, J. Vicente Egas-López and Y. Ma, “CAGE: A Hybrid Curiosity-driven Exploration Strategy Based on Graph for Explore-Exploit Problem in Reinforcement Learning”, IEEE Access 2024 (JCR Q2, 2023 JIF 3.4)
    10. T. Liu, S-N Lai, X. Yuan, Y. Liu and C-T Lam, “A Novel Blockchain-Watermarking Mechanism Utilizing Interplanetary File System and Fast Walsh Hadamard Transform”, iScience, 2024 (JCR Q1, 2023 JIF 4.6)
    11. F. Wang, X. Yuan, Y. Liu and C-T Lam, “LungNeXt: A Novel Lightweight Network utilizing Enhanced Mel-spectrogram for Lung Sound Classification”, Journal of King Saud University - Computer and Information Sciences, 2024 (JCR Q1, 2023 JIF 5.2)
    12. B. Fan, H. Ma, Y. Liu*, and X. Yuan. "BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition", Applied Sciences 14, no. 1: 454. https://doi.org/10.3390/app14010454 (JCR Q2, 2022 JIF 2.7)
    13. B. Fan, H. Ma, Y. Liu*, X. Yuan and W. Ke, “KDTM Multi-stage Knowledge Distillation Transfer Model for Long-tailed DGA Detection Problem”, Mathematics 12, no. 5: 626. https://doi.org/10.3390/math12050626 (JCR Q1, 2022 JIF 2.4)

  • Description

    Yue Liu (Member, IEEE) received the B.Sc. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 2010, and the Ph.D. degree in electronic engineering from Queen Mary University of London, London, U.K., in 2014. She joined MPI-QMUL Information Systems Research Centre, Macau, China, in 2014, as a Researcher and has been working as a Lecturer (Assistant Professor) with the Faculty of Applied Sciences, Macau Polytechnic University, Macau, China, since 2015. Her research interests include Radio Resource Management, Wireless Human Sensing, Deep Reinforcement Learning and AI-driven Wireless Networks and Image Processing. Dr. Liu currently serves as a Reviewer for IEEE Communications Letters, IEEE Transactions on Learning Technologies, and IEEE VTC, as well as a Symposium Chair for IEEE ICSPS.

  • Journal papers

    Journal paper

    1. C. Liu, Y. Hao, Y. Liu*, X. Zhang and X. Wang and Y. Liu, "LWiHS: A Lightweight WiFi-enabled Human Sensing Using Feature Fusion Strategy," in IEEE Transactions on Instrumentation & Measurement. (JCR, Q1, 2023 JIF 5.6)
    2. Z. Wang, M. Luo, X. Xie, Y. Sun, X. Tian, Z. Chen, J. Xie, Q. Gao, T. Tong, Y. Liu and T. Tan, “MMSC-Net:Unified Multi-Scale Spatial Channel Network for End-End Industrial Defect Detection”, IEEE Access 2024 (JCR Q2, 2023 JIF 3.4) 
    3. S. Lv, M. Li, C. Liu, X. Xu, R. Meng, Y. Liu and Y. Liu, "Short-Packet Transmission in NOMA-ISAC Systems", in IEEE Transactions on Cognitive Communications and Networking ( JCR Q1, 2023 JIF 7.4)
    4. X. Xu, X. Mu, Y. Liu, H. Xing, Y. Liu, A. Nallanathan, "Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective," in IEEE Communications Magazine, doi: 10.1109/MCOM.001.2400284. (JCR Q1, 2022 JIF 12.7)
    5. Ji Wang, Jiayi Sun, Fang Wei, Zhao Chen, Y. Liu and Yuanwei Liu, “Deep Reinforcement Learning for Near-Field Wideband Beamforming in STAR-RIS Networks”, Front Inform Technol. Electron. Eng. 25, 1651–1663 (2024). https://doi.org/10.1631/FITEE.2400364 (JCR Q2, 2023 JIF 2.7)
    6. Y. Yu, H. Li, D. Wong, Y. Ma and Y. Liu*, "POMA-C: A Framework for Solving the Problem of Precise Anesthesia Control Under Incomplete Observation Environment in Low-Income Areas," in IEEE Access, vol. 13, pp. 4098-4116, 2025, doi: 10.1109/ACCESS.2024.3524262. (JCR Q2, 2023 JIF 3.4)
    7. Y. Zou, Y. Liu, X. Mu, X. Zhang, Y. Liu* and C. Yuen, "Machine Learning in RIS-assisted NOMA IoT Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3245288. (JCR, Q1, 2022 JIF 10.6)
    8. Y. Wang, Y. Yu, Y. Liu*, Y. Ma and P. Pang, "Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach", JIMR Mental Health 2023, doi: 10.2196/49894 (JCR Q1, 2022 JIF 5.2)
    9. Y. Yu, Y. Liu, D. Wong, H. Li, J. Vicente Egas-López and Y. Ma, “CAGE: A Hybrid Curiosity-driven Exploration Strategy Based on Graph for Explore-Exploit Problem in Reinforcement Learning”, IEEE Access 2024 (JCR Q2, 2023 JIF 3.4)
    10. T. Liu, S-N Lai, X. Yuan, Y. Liu and C-T Lam, “A Novel Blockchain-Watermarking Mechanism Utilizing Interplanetary File System and Fast Walsh Hadamard Transform”, iScience, 2024 (JCR Q1, 2023 JIF 4.6)
    11. F. Wang, X. Yuan, Y. Liu and C-T Lam, “LungNeXt: A Novel Lightweight Network utilizing Enhanced Mel-spectrogram for Lung Sound Classification”, Journal of King Saud University - Computer and Information Sciences, 2024 (JCR Q1, 2023 JIF 5.2)
    12. B. Fan, H. Ma, Y. Liu*, and X. Yuan. "BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition", Applied Sciences 14, no. 1: 454. https://doi.org/10.3390/app14010454 (JCR Q2, 2022 JIF 2.7)
    13. B. Fan, H. Ma, Y. Liu*, X. Yuan and W. Ke, “KDTM Multi-stage Knowledge Distillation Transfer Model for Long-tailed DGA Detection Problem”, Mathematics 12, no. 5: 626. https://doi.org/10.3390/math12050626 (JCR Q1, 2022 JIF 2.4)

     

    Book chapter

    1.  Y. Liu, X. Yang, L. Cuthbert, "Network Slicing with Spectrum Sharing," in Radio Access Network Slicing and Virtualization for 5G Vertical Industries, IEEE, 2021, pp.137-166, doi: 10.1002/9781119652434.ch8.

  • Conference Papers

    1. S. Wang, Z. Ma, K-H Chan, Y. Liu, T. Tong, Q. Gao, G. Zhai, X. Liu and T. Tan, "Contrastive Learning via Randomly Generated Deep Supervision," ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5, doi: 10.1109/ICASSP49660.2025.10890867.
    2. S-P Fong, Y. Liu, C. Liu, and Z. Ding "Deep learning-based human activity recognition using Wi-Fi signals", Proc. SPIE 13559, Sixteenth International Conference on Signal Processing Systems (ICSPS 2024), 1355932 (31 March 2025); https://doi.org/10.1117/12.3060752
    3. F. He, Y. Liu, and J. Liu, "ECA-ViT: Leveraging ECA and Vision Transformer for Crop Leaves Diseases Identification in Cultivation Environments," in 2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), Zhuhai, China, 2024, pp. 101-104, doi: 10.1109/MLISE62164.2024.10674238.
    4. C. Liu, Y. Liu, Y. Hao, and X. Zhang, "LiteWiHAR: A Lightweight WiFi-based Human Activity Recognition System," in 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), Singapore, Singapore, 2024, pp. 1-5, doi: 10.1109/VTC2024-Spring62846.2024.10683210.
    5. N. Xue, X. Mu, Yue Liu, Y. Liu, and Y. Chen, "Hybrid NOMA Empowered Integrated Sensing and Communications," in 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, 2023, pp. 1648-1653, doi: 10.1109/ICCWorkshops57953.2023.10283560.
    6. J. Zhang and Y. Liu, "Deep Learning-based End-to-End Address Recognition Solution on Chinese Courier Order Forms," in 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms (AI2A '23), July 21-23, 2023, Beijing, China. ACM, New York, NY, USA, pp. 158-163, doi: 10.1145/3611450.3611473.
    7. M. Ma, Y. Liu, and K. L. Eddie Law, "UL-CNN: An Unsupervised CNN Model for User Association in Wireless Networks," in 2023 8th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2023, pp. 866-870, doi: 10.1109/ICSIP57908.2023.10271050.
    8. X. Liu, J. Ross, Y. Liu, and Y. Liu, "Asynchronous Personalized Learning for Heterogeneous Wireless Networks," in 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Shanghai, China, 2023, pp. 81-85, doi: 10.1109/SPAWC53906.2023.10304529.
    9. Z. Du, Y. Liu, Y. Yu, and L. Cuthbert, "Time-variant Resource Allocation in Multi-Ap802.11be Network: A DDPG-based Approach," in 2023 8th International Conference on Computer and Communication Systems (ICCCS), Guangzhou, China, 2023, pp. 274-279, doi: 10.1109/ICCCS57501.2023.10150600.
    10. Y. Yu, Y. Ma, Y. Liu, D. Wong, K. Lei, and J. Vicente Egas-López, "Measuring the State-Observation-Gap in POMDPs: An Exploration of Observation Confidence and Weighting Algorithms," in Artificial Intelligence Applications and Innovations, I. Maglogiannis, L. Iliadis, J. MacIntyre, and M. Dominguez, Eds., vol. 675, Springer, Cham, 2023, pp. 137-148, doi: 10.1007/978-3-031-34111-3_13.
    11. Y. Zou, W. Yi, X. Xu, Y. Liu, K. K. Chai, and Y. Liu, "Adaptive NGMA Scheme for IoT Networks: A Deep Reinforcement Learning Approach," in ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 991-996, doi: 10.1109/ICC45041.2023.10278912.
    12. Y. Yu, D. Wong, Y. Ma, and Y. Liu, "MDPG: Markov Decision Process with Graph Representation in Reinforcement Learning," in 2023 International Conference on Algorithms, Computing and Data Processing (ACDP), Qingdao, China, 2023, pp. 121-125, doi: 10.1109/ACDP59959.2023.00026.
    13. Y. Yu, Y. Liu, D. Wong, and S. K. Tang, “Comprehensive Performance Evaluation of Mobile Networks in Macao Based on Field Test Data,” in Proceedings of Eighth International Congress on Information and Communication Technology, X. S. Yang, R. S. Sherratt, N. Dey, and A. Joshi, Eds. Singapore: Springer, 2024, vol. 695, pp. 171–181. doi: 10.1007/978-981-99-3043-2_14.
    14. Y. Liu, L. Zhou, and L. Cuthbert, “Re-sit Exam Timetabling System using a Tailored Genetic Algorithm,” in 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Hung Hom, Hong Kong, 2022, pp. 1-4. doi: 10.1109/TALE54877.2022.00116.
    15. Y. Liu, Y. Yu, Z. Du, and L. Cuthbert, “Sequential State Q-learning Uplink Resource Allocation in Multi-AP 802.11be Network,” in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, UK, 2022, pp. 1-5. doi: 10.1109/VTC2022-Fall57202.2022.10013045.
    16. B. Fan, Y. Liu, and L. Cuthbert, “Improvement of DGA Long Tail Problem Based on Transfer Learning,” in Computer and Information Science. ICIS 2022, Studies in Computational Intelligence, vol. 1055. Cham: Springer, 2022. doi: 10.1007/978-3-031-12127-2_10.
    17. X. Yang, Y. Wang, I. C. Wong, Y. Liu, and L. Cuthbert, “Genetic Algorithm in Resource Allocation of RAN Slicing with QoS Isolation and Fairness,” in 2020 IEEE Latin-American Conference on Communications (LATINCOM), 2020, pp. 1-6. doi: 10.1109/LATINCOM50620.2020.9282290.
    18. X. Yang, Y. Liu, I. C. Wong, Y. Wang, and L. Cuthbert, “Genetic Algorithm for Inter-Slice Resource Management in 5G Network with Isolation,” in 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2020, pp. 1-6. doi: 10.23919/SoftCOM50211.2020.9238298.
    19. Y. Liu, X. Yang, I. C. Wong, Y. Wang, and L. Cuthbert, “Evaluation of Game Theory for Centralized Resource Allocation in Multi-Cell Network Slicing,” in 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019, pp. 1-6. doi: 10.1109/PIMRC.2019.8904365.
    20. X. Yang, Y. Liu, I. C. Wong, Y. Wang, and L. Cuthbert, “Effective isolation in dynamic network slicing,” in 2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019, pp. 1-6. doi: 10.1109/WCNC.2019.8885563.
    21. S. T. Cheong, J. Xu, and Y. Liu, “On the design of web crawlers for constructing an efficient Chinese-Portuguese bilingual corpus system,” in 2018 International Conference on Electronics, Information, and Communication (ICEIC), 2018, pp. 1-4. doi: 10.23919/ELINFOCOM.2018.8330698.
    22. X. Yang, Y. Liu, K. S. Chou, and L. Cuthbert, “QoS-aware power allocation for spectrum sharing,” in 2017 International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT), 2017, pp. 1-6. doi: 10.1109/MoWNet.2017.8045958.
    23. X. Yang, Y. Liu, K. S. Chou, and L. Cuthbert, “A game-theoretic approach to network slicing,” in 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), 2017, pp. 1-4. doi: 10.1109/ATNAC.2017.8215397.
    24. Y. Liu, X. Yang, K. S. Chou, and L. Cuthbert, “Cognitive radio using spectrum-sharing and power minimisation,” in 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2017, pp. 1-6. doi: 10.1109/WoWMoM.2017.7974324.
    25. Y. Liu, X. Yang, K. Chou, “Flexible Spectrum Sharing in OFDMA Cellular Networks,” in Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless Access (ACM MobiWac’16), Nov. 2016, pp. 67-74. doi: 10.1145/2989250.2989262.
    26. Y. Liu and L. Cuthbert, “QoS-guaranteed spectrum sharing in LTE networks,” in 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 2015, pp. 222-227. doi: 10.1109/PACRIM.2015.7334838.
    27. A. Li, Y. Liu, L. Cuthbert, Y. Gao, and Y. Wang, “Optimizing radio resources for heterogeneous QoS-aware OFDMA networks using semi-smart antennas,” in 2014 IEEE International Conference on Communication Systems, 2014, pp. 112-116. doi: 10.1109/ICCS.2014.7024776.
    28. Y. Liu, L. Cuthbert, X. Yang, and Y. Wang, “Delivering Individualised QoS on Spectrum-Sharing OFDMA Networks,” in 2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS), June 2013, Montreal, QC, Canada.
    29. Y. Wang, X. Yang, Y. Zhao, Y. Liu, and L. Cuthbert, “Bluetooth positioning using RSSI and triangulation methods,” in 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), 2013, pp. 837-842. doi: 10.1109/CCNC.2013.6488558.
    30. Y. Liu, L. Cuthbert, X. Yang, and Y. Wang, “QoS-aware radio resource allocation for multi-cell OFDMA network,” in 2012 IEEE International Conference on Communication Systems (ICCS), 2012, pp. 408-412. doi: 10.1109/ICCS.2012.6406180.
    31. Y. Liu, L. Cuthbert, X. Yang, and Y. Wang, “QoS-aware Resource Allocation for Multimedia Users in a Multi-cell Spectrum Sharing Radio Network,” in The 7th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks (PM2HW2N '12), Oct. 2012, pp. 45-52. doi: 10.1145/2387191.2387199.
    32. Y. Liu and L. Cuthbert, “Adaptive Intra Update for H. 264 Video Transmissions over Cognitive Radio Networks,” in The IASTED International Conference on Wireless Communications (IASTED WC2011), June 2011, Vancouver, B.C., Canada. doi: 10.2316/P.2011.730-041.

  • Professional Membership:

    MIEEE

    MIET

    Member of Macao Computer Society