Staff profile

Zhuangkun is an Assistant Professor in the Department of Engineering at Durham University, and also an Academic Visiting at the Department of Computing, Imperial College London. His research interests cover machine learning, and signal processing for wireless communications and UAV controls.  He has published over 30 papers in top IEEE, ACM, OSA, and Nature Group Publications (including 8 first-authored IEEE Transactions). The key research of Zhuangkun are:

• Pointed out a new eavesdropping threat from the malicious usages of the advancing meta-surfaces that are designed for future 6G communications [PaperLink]. Then, an explainable adversarial learning-based physical layer secret key generation framework is proposed to address such eavesdropping [PaperLink]. Leveraging these outcomes, Zhuangkun led the postdoc researchers of Pillar 2 of the CHEDDAR project, collaborating with the groups of Prof. Qammer from Glasgow University, Prof. Ivan from Cranfield University, etc. for 6G demo in communications. 
• Proposed control layer security: theory [PaperLink] and demos [DemoLink], to generate cipher keys to secure UAV-UAV communications, leveraging the cooperative intents and the common aerodynamics of swarm. 
• Proposed to inversely infer the intents of a third-party UAV via its trajectory: control-physics informed inverse reinforcement learning (IRL) framework [PaperLink],[PaperLink]. 
• Proposed graph sampling methodologies for sampling the nonlinear networked dynamics (e.g., water-pipe contaminant monitoring [PaperLink]) [PaperLink]. 
• Another area Zhuangkun worked on is molecular communications, focusing on the computational efficiency algorithms to address inter-symbol interference (ISI) given the propagation of molecules [PaperLink], [PaperLink].

• The key research of Zhuangkun are:
• •Pointed out a new eavesdropping threat from the malicious usages of the advancing meta-surfaces that are designed for future 6G communications [1]. Then, an explainable adversarial learning-based physical layer secret key generation framework is proposed to address such eavesdropping [2]. Leveraging these outcomes, Zhuangkun led the postdoc researchers of Pillar 2 of the CHEDDAR project, collaborating with the groups of Prof. Qammer from Glasgow University, Prof. Ivan from Cranfield University, etc. for 6G demo in communications.
• •Proposed control layer security: theory [3] and demos, to generate cipher keys to secure UAV-UAV communications, leveraging the cooperative intents and the common aerodynamics of swarm. The proof-of-concept demo helped Zhuangkun win industrial support for future fellowships and grant applications.
• •Proposed to inversely infer the intents of a third-party UAV via its trajectory: control-physics informed inverse reinforcement learning (IRL) framework [4],[5].
• •Proposed graph sampling methodologies for sampling the nonlinear networked dynamics (e.g., water-pipe contaminant monitoring [6]) [7]. Based on these works, we entered the Bell Labs Prize Semi-Finalist 2019.
• •Another area Zhuangkun worked on is molecular communications, focusing on the computational efficiency algorithms to address inter-symbol interference (ISI) given the propagation of molecules [8], [9],.
• Refs:
• [1] Z. Wei, B. Li and W. Guo, "Adversarial Reconfigurable Intelligent Surface Against Physical Layer Key Generation," IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2368-2381, 2023. (JCR Q1 IF 6.3)
• [2] Z. Wei, W. Hu, J. Zhang, W. Guo, J. McCann, "Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface", accepted by IEEE Transactions on Wireless Communications, 2025. (JCR Q1, IF 8.9)
• [3] Z. Wei and W. Guo, "Control Layer Security: Exploiting Unobservable Cooperative States of Autonomous Systems for Secret Key Generation," IEEE Transactions on Mobile Computing, vol. 23, no. 10, pp. 9989-10000, Oct. 2024. (JCR Q1 IF 7.7)
• [4] A Perrusquia, W Guo, B Fraser, Z Wei, “Uncovering Drone Intentions using Control Physics Informed Machine Learning” Communications Engineering (Nature Group Publications), vol. 3, no. 36, 2024.
• [5] Z. Wei, B. Li, W. Guo, W. Hu and C. Zhao, "On the Accuracy and Efficiency of Sensing and Localization for Robotics," IEEE Transactions on Mobile Computing, vol. 21, no. 7, pp. 2480-2492, 2022. (JCR Q1 IF 7.7)
• [6] Z. Wei, A Pagani, G Fu, I Guymer, W Chen, J McCann, W Guo, "Optimal Sampling of Water Distribution Network Dynamics Using Graph Fourier Transform," IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 1570-1582, 2020. (JCR Q1 IF 6.7)
• [7] Z. Wei, B. Li, C. Sun and W. Guo, "Sampling and Inference of Networked Dynamics Using Log-Koopman Nonlinear Graph Fourier Transform," IEEE Transactions on Signal Processing, vol. 68, pp. 6187-6197, 2020. (JCR Q1 IF 4.6)
• [8] Z. Wei, B. Li, W. Guo, W. Hu and C. Zhao, "Sequential Bayesian Detection of Spike Activities from Fluorescence Observations," IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 5, no. 1, pp. 3-18, Oct. 2019. (JCR Q3, IF 2.4)
• [9] Z. Wei, W. Guo, B. Li, J. Charmet and C. Zhao, "High-Dimensional Metric Combining for Non-Coherent Molecular Signal Detection," IEEE Transactions on Communications, vol. 68, no. 3, pp. 1479-1493, March 2020. (JCR Q1, IF 7.2)

Conference Paper

• Patil, P., Wei, Z., Petrunin, I., & Guo, W. (2024, June). Classification of RF Transmitters in the Presence of Multipath Effects Using CNN-LSTM. Presented at 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA
• Wang, L., Wei, Z., & Guo, W. (2023, July). Securing IoT Communication Using Physical Sensor Data — Graph Layer Security with Federated Multi-agent Deep Reinforcement Learning. Presented at 2023 8th International Conference on Signal and Image Processing (ICSIP), Wuxi, China
• Hu, W., Wei, Z., Leeson, M., & Xu, T. (2022, November). Eavesdropping Against Bidirectional Physical Layer Secret Key Generation in Fiber Communications. Presented at 2022 IEEE Photonics Conference (IPC), Vancouver, BC, Canada
• Wei, Z., Wang, L., & Guo, W. (2022, September). Secret Key Rate Upper-bound for Reconfigurable Intelligent Surface-combined System under Spoofing. Presented at 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, United Kingdom
• Sun, S. C., Jin, B., Wei, Z., & Guo, W. (2022, July). Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process. Presented at Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, Vienna, Austria
• Sun, S. C., Li, C., Wei, Z., Tsourdos, A., & Guo, W. (2021, February). Scalable Partial Explainability in Neural Networks via Flexible Activation Functions (Student Abstract). Presented at AAAI-21: AAAI Conference on Artificial Intelligence, Online
• Guo, W., Wei, Z., & Li, B. (2020, October). Secure Internet-of-Nano Things for Targeted Drug Delivery: Distance-based Molecular Cipher Keys. Presented at 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME), Amman, Jordan
• Wei, Z., Pagani, A., & Guo, W. (2019, October). Monitoring Networked Infrastructure with Minimum Data via Sequential Graph Fourier Transforms. Presented at 2019 IEEE International Smart Cities Conference (ISC2), Casablanca, Morocco
• Wei, Z., Zhang, Y., Li, B., & Zhao, C. (2017, July). Localization Scheme with MAP Pre-filter in Wireless Sensor Network Combating Intensive Measurement Noise. Presented at CSPS 2017: Communications, Signal Processing, and Systems, Harbin, China
• Wei, Z., Hu, W., Zhang, M., Han, D., Li, B., & Zhao, C. (2017, November). Viterbi Estimation on the Finite-State Markov Ultra-violet Channels. Presented at Asia Communications and Photonics Conference 2017, Guangzhou, Guangdong
• Wei, Z., Li, B., & Zhao, C. (2014, July). Polar Code for Future 60 GHz Millimeter-Wave Communications. Presented at Third International Conference on Communications, Signal Processing, and Systems, Hohhot, Inner Mongolia

Journal Article

• Chen, Y., Al-Rubaye, S., Tsourdos, A., Chu, K.-F., Wei, Z., Baker, L., & Gillingham, C. (online). Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/tits.2024.3463189
• Wei, Z., Hu, W., Zhang, J., Guo, W., & McCann, J. (online). Explainable Adversarial Learning Framework on Physical Layer Key Generation Combating Malicious Reconfigurable Intelligent Surface. IEEE Transactions on Wireless Communications, https://doi.org/10.1109/twc.2025.3531799
• Perrusquía, A., Wei, Z., & Guo, W. (2024). Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(12), 7897-7908. https://doi.org/10.1109/tsmc.2024.3462790
• Wei, Z., & Guo, W. (2024). Control Layer Security: Exploiting Unobservable Cooperative States of Autonomous Systems for Secret Key Generation. IEEE Transactions on Mobile Computing, 23(10), 9989-10000. https://doi.org/10.1109/tmc.2024.3369754
• Zhang, Z., Wei, Z., Court, S., Yang, L., Wang, S., Thirunavukarasu, A., & Zhao, Y. (2024). A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry. Buildings, 14(4), Article 1113. https://doi.org/10.3390/buildings14041113
• Guo, W., Wei, Z., Gonzalez, O., Perrusquía, A., & Tsourdos, A. (2024). Control Layer Security: A New Security Paradigm for Cooperative Autonomous Systems. IEEE Vehicular Technology Magazine, 19(1), 93-102. https://doi.org/10.1109/mvt.2023.3290773
• Perrusquía, A., Guo, W., Fraser, B., & Wei, Z. (2024). Uncovering drone intentions using control physics informed machine learning. Communications Engineering, 3(1), Article 36. https://doi.org/10.1038/s44172-024-00179-3
• Qiu, S., Wei, Z., Huang, Y., Abbaszadeh, M., Charmet, J., Li, B., & Guo, W. (2024). Review of Physical Layer Security in Molecular Internet of Nano-Things. IEEE Transactions on NanoBioscience, 23(1), 91-100. https://doi.org/10.1109/tnb.2023.3285973
• Li, C., Sun, S. C., Wei, Z., Tsourdos, A., & Guo, W. (2023). Scarce data driven deep learning of drones via generalized data distribution space. Neural Computing and Applications, 35(20), 15095-15108. https://doi.org/10.1007/s00521-023-08522-z
• Huang, Y., Wen, M., Lin, L., Li, B., Wei, Z., Tang, D., Li, J., Duan, W., & Guo, W. (2023). Physical-Layer Counterattack Strategies for the Internet of Bio-Nano Things with Molecular Communication. IEEE Internet of Things Magazine, 6(2), 82-87. https://doi.org/10.1109/iotm.001.2300029
• Lin, Z., Li, B., Wei, Z., Huang, Y., Guo, W., & Zhao, C. (2023). Diversity-Based Non-Coherent Signal Detector for Molecular Communication via Reaction-Diffusion. IEEE Transactions on Communications, 71(5), 2618-2631. https://doi.org/10.1109/tcomm.2023.3249785
• Wei, Z., Li, B., & Guo, W. (2023). Adversarial Reconfigurable Intelligent Surface Against Physical Layer Key Generation. IEEE Transactions on Information Forensics and Security, 18, 2368-2381. https://doi.org/10.1109/tifs.2023.3266705
• Hu, W., Wei, Z., Popov, S., Leeson, M., & Xu, T. (2023). Tapping Eavesdropper Designs Against Physical Layer Secret Key in Point-to-Point Fiber Communications. Journal of Lightwave Technology, 41(5), 1406-1414. https://doi.org/10.1109/jlt.2022.3223025
• Zong, Y., Dai, X., Wei, Z., Zou, M., Guo, W., & Gao, Z. (2023). Robust Time Synchronization for Industrial Internet of Things by H ∞ Output Feedback Control. IEEE Internet of Things Journal, 10(3), 2021-2030. https://doi.org/10.1109/jiot.2022.3144199
• Wei, Z., Li, B., Guo, W., Hu, W., & Zhao, C. (2022). On the Accuracy and Efficiency of Sensing and Localization for Robotics. IEEE Transactions on Mobile Computing, 21(7), 2480-2492. https://doi.org/10.1109/tmc.2020.3038146
• Wei, Z., Guo, W., & Li, B. (2022). A Multi-Eavesdropper Scheme Against RIS Secured LoS-Dominated Channel. IEEE Communications Letters, 26(6), 1221-1225. https://doi.org/10.1109/lcomm.2022.3166239
• Pagani, A., Wei, Z., Silva, R., & Guo, W. (2022). Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics. ACM Transactions on Internet Technology, 22(1), 1-18. https://doi.org/10.1145/3461838
• Guo, W., Abbaszadeh, M., Lin, L., Charmet, J., Thomas, P., Wei, Z., Li, B., & Zhao, C. (2021). Molecular Physical Layer for 6G in Wave-Denied Environments. IEEE Communications Magazine, 59(5), 33-39. https://doi.org/10.1109/mcom.001.2000958
• Huang, Y., Ji, F., Wei, Z., Wen, M., & Guo, W. (2021). Signal Detection for Molecular Communication: Model-Based vs. Data-Driven Methods. IEEE Communications Magazine, 59(5), 47-53. https://doi.org/10.1109/mcom.001.2000957
• Huang, Y., Ji, F., Wei, Z., Wen, M., Chen, X., Tang, Y., & Guo, W. (2021). Frequency Domain Analysis and Equalization for Molecular Communication. IEEE Transactions on Signal Processing, 69, 1952-1967. https://doi.org/10.1109/tsp.2021.3066069
• Liu, S., Wei, Z., Wang, X., & Zhao, C. (2020). Channel Capacity Analysis of a Comprehensive Absorbing Receiver for Molecular Communication via Diffusion. IEEE Access, 8, 227152-227160. https://doi.org/10.1109/access.2020.3045439
• Wei, Z., Li, B., Sun, C., & Guo, W. (2020). Sampling and Inference of Networked Dynamics Using Log-Koopman Nonlinear Graph Fourier Transform. IEEE Transactions on Signal Processing, 68, 6187-6197. https://doi.org/10.1109/tsp.2020.3032408
• Hu, W., Wei, Z., Popov, S., Leeson, M., Zhang, M., & Xu, T. (2020). Non-Coherent Detection for Ultraviolet Communications With Inter-Symbol Interference. Journal of Lightwave Technology, 38(17), 4699-4707. https://doi.org/10.1109/jlt.2020.2993537
• Liu, S., Wei, Z., Li, B., & Zhao, C. (2020). Unsupervised Clustering-Based Non-Coherent Detection for Molecular Communications. IEEE Communications Letters, 24(8), 1687-1690. https://doi.org/10.1109/lcomm.2020.2985073
• Wei, Z., Pagani, A., Fu, G., Guymer, I., Chen, W., McCann, J., & Guo, W. (2020). Optimal Sampling of Water Distribution Network Dynamics Using Graph Fourier Transform. IEEE Transactions on Network Science and Engineering, 7(3), 1570-1582. https://doi.org/10.1109/tnse.2019.2941834
• Wei, Z., Pagani, A., Li, B., & Guo, W. (2020). Monitoring Embedded Flow Networks Using Graph Fourier Transform Enabled Sparse Molecular Relays. IEEE Communications Letters, 24(5), 986-990. https://doi.org/10.1109/lcomm.2020.2978835
• Wei, Z., Guo, W., Li, B., Charmet, J., & Zhao, C. (2020). High-Dimensional Metric Combining for Non-Coherent Molecular Signal Detection. IEEE Transactions on Communications, 68(3), 1479-1493. https://doi.org/10.1109/tcomm.2019.2959354
• Wei, Z., Li, B., Hu, W., Guo, W., & Zhao, C. (2020). Hamming–Luby rateless codes for molecular erasure channels. Nano Communication Networks, 23, Article 100280. https://doi.org/10.1016/j.nancom.2019.100280
• Wei, Z., Li, B., & Guo, W. (2019). Optimal Sampling for Dynamic Complex Networks With Graph-Bandlimited Initialization. IEEE Access, 7, 150294-150305. https://doi.org/10.1109/access.2019.2946899
• Wei, Z., Li, B., Guo, W., Hu, W., & Zhao, C. (2019). Sequential Bayesian Detection of Spike Activities From Fluorescence Observations. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 5(1), 3-18. https://doi.org/10.1109/tmbmc.2019.2943288
• Liu, S., Wei, Z., Li, B., Guo, W., & Zhao, C. (2019). Metric combinations in non-coherent signal detection for molecular communication. Nano Communication Networks, 20, 1-10. https://doi.org/10.1016/j.nancom.2019.02.001
• Wei, Z., Hu, W., Han, D., Zhang, M., Li, B., & Zhao, C. (2018). Simultaneous channel estimation and signal detection in wireless ultraviolet communications combating inter-symbol-interference. Optics Express, 26(3), 3260-3270. https://doi.org/10.1364/oe.26.003260
• Wei, Z., Li, B., & Zhao, C. (2015). On the polar code for the 60-GHz millimeter-wave systems. EURASIP Journal on Wireless Communications and Networking, 2015(1), Article 31. https://doi.org/10.1186/s13638-015-0264-y