Assoc. Prof. Chuanting Zhang

Shandong University, China

Biography: Chuanting Zhang is an associate professor at Shandong University, Jinan, China. He previously worked as a senior postdoctoral researcher at the University of Bristol and a postdoctoral researcher at King Abdullah University of Science and Technology (KAUST). His research interests encompass spatiotemporal data mining and intelligent networks. He has led several projects, including those funded by national talent programs, the National Natural Science Foundation of China, and the Shandong Province Excellent Youth Science Fund Project. He has proposed a series of wireless traffic prediction algorithms, with publications in top-tier journals and conferences, including IEEE JSAC and IEEE INFOCOM. His research achievements have been recognized with awards such as the IEEE ICCT Young Scientist Award, the Shandong Province Excellent Doctoral Dissertation Award, the Excellent Doctoral Dissertation Award of the Shandong Artificial Intelligence Society, and the Best Paper Award at IEEE SmartData. He is a Senior Member of IEEE, a Member of ACM, and a committee member of the CCF Technical Committee on Network and Data Communications.
Personal Website: https://faculty.sdu.edu.cn/ctzhang

 

Associate Researcher Shuai Ma

Peng Cheng Laboratory, China

Speech Title: Theory and Key Technologies of Reliable Semantic Communication for 6G

Abstract: Semantic communication focuses on semantic-level representation and transmission of information, which provides a potential solution for the sustainable development of 6G. However, its practical applications are facing serious challenges , such as the lack of semantic channel capacity theory and low reliability of the target semantic information extraction and transmission. To address the above challenges, we derived a semantic channel coding theorem, proposed a robust information bottleneck theory , proposed an Alpha-Beta-Gamma (ABG) formula to model the relationship between the end-to-end measurement and SNR, and developed a semantic feature division multiple access (SFDMA) paradigm for multi-user semantic networks.

Biography: Shuai Ma received the B.S. and Ph.D. degrees in communication and information systems from Xidian University, Xi'an, China, in 2009 and 2016, respectively. From 2014 to 2015, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA. From 2016 to 2019, he has been an associate Professor with the School of Information and Control Engineering, at the China University of Mining and Technology, Xuzhou, China. From 2019 to 2022, he worked as a Postdoctoral Fellow with Telecom Paris, France. Since 2023, he has been an Associate Researcher at Peng Cheng Laboratory, Shenzhen, China. His research interests include semantic communications, visible light communications, and network information theory.

 

Assoc. Prof. Mingjie Shao

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

Speech Title: Quantized Signal Processing in Massive MIMO: Identifiability, Optimization, and Deep Learning Algorithms

Abstract: In this talk, we introduce quantized signal processing in massive MIMO systems, driven by the need to use low-resolution DACs/ADCs to reduce power consumption. However, coarse quantization results in the loss of amplitude information from communication signals, making signal estimation and detection challenging. We present formulations for maximum-likelihood estimation (MLE) and discuss the associated challenges with integrals and nonsmooth objective functions. Identifiability conditions for quantized signal sensing are introduced, quantifying the relationship between the number of measurements and the parameter dimension. Then, we propose novel global optimization algorithms for both signal detection and channel estimation. To enhance performance and efficiency, we incorporate a deep unfolding adaptation, supported by a theoretical analysis of the activation function. Simulation results demonstrate the effectiveness of our approaches.

Biography: Mingjie Shao received a bachelor’s degree from Xidian University, China, in 2015, as part of the “Excellent Engineer Education Program.” In 2020, he earned a Ph.D. in Electronic Engineering from the Chinese University of Hong Kong, supported by the “Hong Kong PhD Fellowship Scheme (HKPFS)” under the supervision of Prof. Wing‑Kin Ma (IEEE Fellow). From 2020 to 2023, he worked as a postdoctoral researcher in the Department of Electronic Engineering at the Chinese University of Hong Kong. He was then a Professor, awarded “Qilu Young Scholar”, in the School of Information Science and Engineering at Shandong University during 2023‑2024. Currently, he is an Associate Professor in the State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences (CAS), and has been awarded the “Chen Jingrun Future Star.”

His main research interests include: 1) Signal processing for wireless communication; 2) Optimization and statistical methods in signal processing and machine learning; 3) Cross‑disciplinary research in deep learning and signal processing. In recent years, he has published over 40 SCI/EI papers in top journals and conferences such as IEEE TSP、IEEE JSTSP、IEEE TIFS and IEEE ICASSP. Several of his papers have been recognized multiple times in the “Top 50 Popular Articles” list of IEEE journals.

 

Assoc. Prof. Qiang He

Northeastern University, China

Biography: Qiang He received the Ph.D. degree in computer application technology from the Northeastern University, Shenyang, China in 2020. He also worked with School of Computer Science and Technology, Nanyang Technical University, Singapore as a visiting PhD researcher from 2018 to 2019. He is currently an Associated Professor at the College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China. His research interests include machine learning, social network analytic, data mining, health care, infectious diseases informatics, etc. He has published more than 70 journal articles and conference papers, including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing, IEEE Transactions on Computational Social Systems, IEEE Transactions on Cognitive and Developmental Systems.

Qiang He is with the School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China, e-mail: heqiang@bmie.neu.edu.cn

 

Assoc. Prof. Xiangping Zhai

Nanjing University of Aeronautics and Astronautics, China

Biography: Dr. Xiangping Bryce Zhai is currently an Associate Professor at Nanjing University of Aeronautics and Astronautics, China. He received his PhD from City University of Hong Kong in 2013. His research interests include the Internet of Intelligent Things, edge computing, resource optimization, communication networks, and smart cooperation. He has published over 60 relevant papers in international journals such as JSAC, TON, TMC, TCOM, and TWC. He has received several awards, including the Second Prize for Young Science and Technology from the Jiangsu Association for Information Technology Applications, the China’s Top 100 Most Influential International Academic Papers Award, and Outstanding Teacher Award for Computer Science Majors in Colleges and Universities by the Ministry of Education. Meanwhile, he has participated in many national and Jiangsu provincial key R&D plans, National Natural Science Foundation, Civil Aviation Administration of China, and other projects.

 

Assoc. Prof. Wei Yang

Shenzhen Technology University, China

Biography: Wei Yang, Ph.D., Associate Professor of Engineering, IEEE Member. He received the Ph.D. degree in information and communication engineering from Beijing University of Posts and Telecommunications (BUPT), Beijing, China. He was also a Research Fellow with the State Key Laboratory of Networking and Switching Technology, BUPT, China. He has served as the TPC of IEEE ICCT (2021-2024), the Session Chair of IEEE WCCCT 2025, and also served as a reviewer for multiple well-known international academic journals and conferences. His research interests include intelligent wireless communication, cyber-physical system, information security and fusion.

 

Assoc. Prof. Liwei Yang

China Agricultural University, China

Speech Title: State-Aware Multi-Service Scheduling for VLC/WiFi Heterogeneous Networks Based on Reinforcement Learning(SMASS-QL)

Abstract: Addressing the challenges of fairness, latency, and throughput balancing in multi-service scheduling within VLC/WiFi heterogeneous networks, as well as the limited adaptability of existing algorithms, this paper proposes a state-aware multi-service scheduling algorithm (SMASS-QL) based on reinforcement learning. This algorithm models the scheduler as an agent, constructing a state space by discretizing queue head delay urgency, queue congestion status, and channel quality. It integrates four classical scheduling strategies to form the action space and designs a composite reward function incorporating incentive mechanisms, achieving dynamic optimization through Q-Learning. Simulation results demonstrate that SMASS-QL maintains a Jain fairness index consistently above 0.985, achieves average packet delays as low as 0.004–0.008 ms, and attains a system throughput approaching 4000 Mbps. By ensuring fairness and low latency while sacrificing only minimal throughput, it provides an efficient solution for resource scheduling in heterogeneous networks.

Biography: Dr. Liwei Yang, professor of China Agricultural University. She received the B.E. degree in Telecommunication Engineering from Chongqing University of Posts and Telecommunications, China, and the Ph.D. degree in Information and Communications Engineering from Beijing University of Posts and Telecommunications, China. From 2009 to 2011, she was a Postdoctoral Research Fellow with the Department of Electronic Engineering, Tsinghua University, China. In 2015, she joined the faculty of the College of Information and Electrical Engineering, China Agricultural University. Her research interests include optical networks, optical wireless communications and visible light communication. She participated in a number of national projects and published more than 100 papers. She served as a TPC member of several international academic conferences and a reviewer for several international journals.

 

Assoc. Prof. Danyang Zheng

Southwest Jiaotong University, China

Biography: Dr. Danyang Zheng is an Assistant Professor at Southwest Jiaotong University. He serves as a Young Editorial Board Member for the journal Big Data Mining and Analytics (Impact Factor: 7.7, SCI Q1 Top). He earned his Bachelor's degree in Computer Science and Technology from the University of Electronic Science and Technology of China in 2016. As a China Scholarship Council awardee, he pursued his PhD at Georgia State University under the supervision of Professor Yi Pan and Professor Xiaojun Cao. He received his doctorate in May 2021, graduating as Outstanding PhD Graduate (top-ranked among 400+ candidates). In August 2021, he joined Soochow University as a University Distinguished Young Scholar. Since January 2023, he has held his current position at Southwest Jiaotong University. He has published over 60 high-impact SCI/EI papers in leading venues including IEEE/ACM ToN, IEEE TDSC, IEEE TCoM, IEEE TNSM, IEEE TCE, IEEE TNSE, IEEE Internet of Things Journal, Computer Networks，IEEE INFOCOM，IEEE ICC，IEEE GLOBECOM, IEEE ICCCN, WCCCT, ICCC. He also serves as the software testing skill competition manager for WorldSkill 2026 Shanghai.

 

Assoc. Prof. Chengzong Peng

Chengdu University of Information Technology, China

Speech Title: k-Connected Slice Protection for Heterogeneous Concurrent Attacks on AIGC Services

Abstract: Artificial Intelligence Generated Content (AIGC) services offer significant advantages in enhancing creativity, optimizing decision-making, and reducing costs. However, the inherent complexity of AIGC deployment, coupled with the diversity and dynamism of attack vectors, poses major challenges to traditional security mechanisms. Among these challenges, heterogeneous concurrent attacks, which target multiple different types of objectives simultaneously, represent the greatest threat. To address this issue, we propose the k-connected AC Slice (KS) strategy, a specialized security architecture designed to resist such attacks while minimizing resource overhead. We formulate this security configuration task as the AIGC Component Security Deployment (ACSD) problem and prove it's NP-hard. To solve the ACSD problem, we introduce a novel optimization algorithm, the Bandwidth-optimized KS Deployment (BKS-D) heuristic. Representative simulations are conducted to evaluate the proposed algorithm, benchmarking it against the SACD and SFSE algorithms across multiple performance metrics. The experimental results demonstrate that the proposed BKS-D algorithm significantly
outperforms state-of-the-art methods in both protection effectiveness and bandwidth resource consumption.

Biography: Chengzong Peng, Ph.D., Associate professor, IEEE member, CCF member. His research focuses on network reliability, cyberspace security, artificial intelligence. He has published over 30 SCI/EI papers, including IEEE INFOCOM, IEEE TNSM, IEEE IoTJ, and Computer Networks. He is currently leading/working on multiple national and provincial-level scientific research projects. He is serving as the TPC of ICNC 2025, and has served as the Session Chair of WCCCT 2024, WCCCT 2025, and the Talk Chair of ACM TURC 2024. He has also served as a reviewer for multiple well-known international academic journals and conferences, such as Big Data Mining and Analytics, Expert System with Applications, and Computer Network.

 

Assoc. Prof. Dongyang Li

China University of Petroleum (Huadong), China

Biography: Dongyang Li is currently an Associate Professor with the College of Oceanography and Space Informatics, China University of Petroleum (Huadong). He received the Ph.D. degrees in information and communication engineering from the School of Information Science and Engineering, Shandong University, Jinan, China, in 2023. He is also with the Shandong Key Laboratory of intelligent Communication and Sensing-Computing Integration, China. He has served as TPC members of ICC'25, ICCC'25. His research interests include AI-enhanced wireless communications, edge computing/caching and UAV Communications.

 

Assoc. Prof. Azhar Imran

Beijing University of Technology, China

Speech Title: Computational Intelligence in Healthcare: Navigating hope vs hype in China

Abstract: Computational Intelligence (CI) has emerged as a transformative force in healthcare, promising unprecedented advances in diagnosis, treatment, and personalized medicine. Techniques such as machine learning, deep learning, natural language processing, and evolutionary algorithms are redefining how clinicians interpret medical data and make decisions. However, alongside the optimism lies considerable hype exaggerated claims, ethical concerns, data biases, and limited clinical validation that often hinder real-world impact. This speech, Computational Intelligence in Healthcare: Hope vs. Hype, explores the fine balance between technological promise and practical limitations. It highlights success stories in predictive diagnostics, drug discovery, and medical imaging while critically addressing challenges related to data quality, model interpretability, regulatory compliance, and patient trust. The discussion aims to separate genuine innovation from inflated expectations, urging researchers and policymakers to adopt a responsible, evidence-driven approach to integrating CI into healthcare systems. Ultimately, the talk emphasizes that the true hope of computational intelligence lies not in replacing clinicians but in empowering them through transparent, ethical, and human-centered AI.

Biography: Dr. Azhar Imran is an Associate Professor of Computer Science at Beijing University of Technology (BJUT), China, and a Senior Member of IEEE. He has over 13 years of academic and research experience in Artificial Intelligence, Data Science, and Machine Learning, with a strong focus on AI-driven healthcare and cybersecurity applications. Dr. Imran has published over 85 research articles in reputed international journals and conferences and has delivered keynote speeches at prestigious AI and biomedical events. He has received several awards, including the Outstanding Graduate Award, Best Researcher Award, and the Embassy Honored Award from the Pakistan Embassy in Beijing. His current research explores explainable AI, multimodal medical imaging, and computational intelligence frameworks for disease prediction and early diagnosis. More about his work can be found at https://sites.google.com/view/azharimran.

 

Assoc. Prof. Hongwei Wang

University of Electronic Science and Technology of China, China

Speech Title: Compressive Near-Field Wideband Channel Estimation for THz Extremely Large-scale MIMO Systems

Abstract: We consider the channel acquisition problem for a wideband terahertz (THz) communication system, where an extremely large-scale array is deployed to mitigate severe path attenuation. In channel modeling, we account for both the spherical wavefront and beam-splitting phenomena of the wideband near-field channel. We propose a frequency-independent orthogonal dictionary that generalizes the standard discrete Fourier transform (DFT) matrix by introducing an additional parameter to capture near-field effects. This dictionary enables an efficient two-dimensional (2D) block-sparse representation of the wideband near-field channel. By leveraging this structured sparsity, the wideband near-field channel estimation problem can be effectively solved within a customized compressive sensing framework. Numerical results demonstrate the significant advantages of our proposed 2D block-sparsity-aware method over conventional polar-domain-based approaches for near-field wideband channel estimation.

Biography: Hongwei Wang received the B.S. and Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China, in 2013 and 2019, respectively. From December 2019 to July 2024, he was a Post-Doctoral Researcher with the University of Electronic Science and Technology of China, where he is currently an Associate Professor. His research interests include statistical signal processing, compressed sensing and sparse theory, and mmWave/THz wireless communications.

 

Lecturer Ruihong Jiang
Beijing University of Posts and Telecommunications, China

Speech Title: Spaceborne Reconfigurable Intelligent Surface-Enabled Reflective Communications: Modeling and Performance

Abstract: As satellite networks evolve toward 6G integration, Reconfigurable Intelligent Surface (RIS)-enabled spaceborne reflective communication has emerged as a transformative paradigm to enhance coverage and energy efficiency through intelligent, passive signal reflection from orbit. This talk explores the theoretical foundations and performance benchmarks of such systems. We begin by establishing a robust channel model tailored to the unique orbital dynamics and atmospheric propagation constraints of space-to-ground links. Based on this, we derive closed-form outage probability expressions and characterize fundamental performance limits for multi-user scenarios under random channel realizations. Furthermore, we address practical implementation challenges, particularly performance degradation under imperfect channel state information, and discuss how AI-driven channel prediction can mitigate these effects. We also outline emerging opportunities in multi-satellite cooperative RIS networks as a key direction for future research. This session aims to provide a comprehensive roadmap for deploying RIS in next-generation satellite constellations.

Biography: Ruihong Jiang received the Ph.D. degree from the School of Computer and Information Technology, Beijing Jiaotong University (BJTU), Beijing, China, in 2021. She has been a lecturer at Beijing University of Posts and Telecommunications since 2022. Her current research interests include satellite/UAV communications, AI-assisted communication, reflection communication, and wireless power transfer communication. She leads a National Natural Science Foundation of China (NSFC) Young Scientists Fund project and has participated in multiple national key projects. She has published over 30 papers and received Best Paper Awards at IEEE PACRIM 2024, IEEE ICC 2021, and CIEIT 2018. She is the author of one academic monograph and holds two granted patents. She serves as Guest Editor for the journal Electronics, Chair of IEEE ICCC ws 2024, and TPC member for IEEE PACRIM 2024 and ICCC 2024, and regularly reviews for leading IEEE journals, including IEEE TWC, IEEE TCOM, and IEEE JSAC, as well as flagship conferences such as IEEE Globecom and IEEE ICC.

 

Lecturer Jiachi Zhang
Shandong Police College, China

Biography: Dr. Jiachi Zhang received the B.Sc., M.Eng., and Ph.D. degrees in Communication Engineering and Communication and Information Systems from Beijing Jiaotong University, Beijing, China, in 2013, 2016, and 2023, respectively. He is currently a lecturer with the School of Police Information at Shandong Police College (SDPC), where he teaches cybersecurity and law enforcement–related courses. He concurrently serves as a police officer within the Shandong public security system. He is also affiliated with the Low Altitude Safety Research Institute and the Shandong Engineering Research Center for Deterministic Network Information Security at SDPC.

Dr. Zhang has authored more than 30 peer-reviewed papers as the first author in leading journals and conferences, including IEEE Transactions on Antennas and Propagation, IEEE Transactions on Vehicular Technology, and IEEE International Conference on Communications, etc. He also holds 9 granted Chinese invention patents (as an inventor or co-inventor). He has served as a principal researcher in sub-projects of national key R&D programs, projects funded by the National Natural Science Foundation of China, the Beijing Natural Science Foundation, and open research projects of national key laboratories. He has been invited to deliver presentations at international academic conferences. In 2025, he received the Second Prize of the Shandong Province Science and Technology Progress Award as a contributing member.

His current research interests focus on wideband channel measurement and modeling for mobile communication systems, including satellite communications, vehicular communications, high-speed railway communications, unmanned aerial vehicle communications, and advanced signal processing techniques.

 

Asst. Prof. Pan Yi
Shenzhen Technology University, China

Biography: Dr. Pan Yi is an Assistant Professor at Shenzhen Technology University, with a doctoral degree in Engineering and postdoctoral research experience at Tsinghua University.
His research focuses on IoT systems, image and positioning perception processing algorithms, and lightweight deployment and optimization of edge AI models. He has published over 10 academic papers in domestic and international journals and conferences, and delivered oral presentations at multiple international conferences. He has presided over and completed projects including an edge AI box for smart logistics canteen supervision, a multi-modal fusion safe driving vehicle-mounted system, WiFi/UWB-based positioning algorithms, and the eNDOS collaborative IoT operating system. The achievements of these projects have achieved industrial transformation with a scale of over 10 million yuan.

 

Asst. Prof. Chen Yang
Southwest Jiaotong University, China

Speech Title: Communication-Efficient Satellite-Ground Federated Learning

Abstract: With the explosive growth of Low Earth Orbit satellite constellations, Earth observation systems are generating massive volumes of data. However, limited bandwidth, short communication windows, and bursty packet loss make the traditional “sense-then-transmit” paradigm increasingly inefficient. Satellite–Ground Federated Learning enables collaborative onboard model tuning on resource-constrained satellites, but its performance is fundamentally restricted by communication bottlenecks. To overcome these limitations, we propose a communication-efficient and packet-loss-tolerant SGFL framework that improves both efficiency and robustness. The framework integrates bandwidth-aware progressive quantization with loss-aware parameter chunking and scheduling. By adaptively assigning heterogeneous bit-widths to model blocks and dynamically coordinating transmission with predicted stable link windows, our approach maximizes bandwidth utilization while mitigating bursty packet loss. Extensive experiments show that our method accelerates convergence by up to 2.8× under bandwidth constraints, improves accuracy by 9%–38% in lossy scenarios, and reduces overall completion time by up to 20× compared with baselines.

Biography: Chen Yang received Ph.D. degree in computer science at the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, in 2025. Currently, he is an assistant professor in School of Computing and Artificial Intelligence, Southwest Jiaotong University. He has published papers in Mobicom, TMC, TSC, etc. His research interests include edge intelligence, federated learning and service computing. He serves as a TPC member for the International Conference on Network and Parallel Computing, and acts as a reviewer for leading international journals/conferences, including IEEE Transactions on Mobile Computing, IEEE Transactions on Services Computing, IEEE International Conference on Web Services.

 

Prof. Qian Sun

China Waterborne Transport Research Institute, Ministry of Transport, China

Speech Title: Development of Maritime Radio Communication and Navigation Technology and International Compliance

Abstract: Addressing the special topic of "Maritime Communication, Sensing, and Computing Integration" at the 2026 IEEE WCCCT, this paper systematically analyzes the evolution path and international compliance practices of maritime radio communication and navigation technologies. The report focuses on the technical substance of the VHF Data Exchange System (VDES) amendments (expected to enter into force in 2028) and the new performance standards (MSC.511/512) for MF/HF and VHF equipment. Targeting the formulation of performance standards for Dual-Frequency Multi-Constellation Satellite-Based Augmentation Systems (DFMC SBAS) and Advanced Receiver Autonomous Integrity Monitoring (ARAIM), which are set to be initiated at NCSR 13 in 2026, the paper discusses technical upgrade directions for shipborne satellite navigation receivers combined with the application of the BeiDou System (BDS). Simultaneously, it interprets the Guidelines for Software Maintenance of Shipborne Communication and Navigation Equipment, which is to be submitted by the International Maritime Organization (IMO) for consideration at MSC 111 in May 2026, analyzing key compliance points regarding remote maintenance, cybersecurity, and lifecycle management. Furthermore, in conjunction with maritime radio spectrum planning and the International Telecommunication Union (ITU) framework, the paper dissects the practical challenges of equipment type approval and multi-standard compliance (e.g., IEC 60945, FCC Part 80), and proposes an integrated solution for communication and navigation systems incorporating edge computing and the Marine Internet of Things (MIoT). Finally, the paper shares China's practical experience in standardization under the frameworks of the IMO and the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA), providing a technical path and implementation reference for the global shipping industry to address digital transformation and international compliance.

Biography: Dr. Sun Qian, female, graduated from Beihang University in 2012. She is currently a Chief Research Fellow at the China Waterborne Transport Research Institute, Ministry of Transport. She also serves as a member of the Leading Group for Radio Management of the Ministry of Transport, a member of the Maritime Communication Counterpart Group of the Ministry of Industry and Information Technology, and a member of the National Technical Committee on BeiDou Satellite Navigation Standardization. She has long been involved in meetings of international organizations such as the International Maritime Organization (IMO), the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA), and the International Telecommunication Union (ITU), responsible for issues related to maritime communication, navigation, and information security.
She currently serves as Vice Chair of the IALA Engineering Committee on Navigation. She is also a member of the Sub-committee on International Maritime Safety of Navigation, Communications and Search and Rescue, the Sub-committee on Maritime Radio Communication, the Sub-committee on Satellite Technology Application, the Sub-committee on Navigational Warnings, and the Aids to Navigation Sub-committee of the Ministry of Transport.
Her main research interests include satellite communication and navigation technology for waterborne transport, maritime navigation safety supervision and guarantee technology, BeiDou application and internationalization in waterborne transport, international rules and coordination for maritime navigation, and new-generation waterborne dedicated digital communication. She has presided over or participated in more than 60 national and ministerial-level research projects, won 3 first prizes, 2 second prizes, and 1 third prize of ministerial-level science and technology awards. She has published more than 30 academic papers, presided over the formulation of 13 international standards and 8 national standards, and obtained 13 authorized national invention patents.

 

Dr. Yanqing Xu

The Chinese University of Hong Kong, Shenzhen, China

Speech Title: Environment-Aware Network-Level Design for Generalized Pinching-Antenna Systems

Abstract: Generalized pinching-antenna (GPA) systems enable a reconfigurable radiation point along a guided medium, offering a new degree of freedom beyond conventional fixed-aperture deployments. While most existing studies focus on link-level optimization for quasi-static users, such designs typically require frequent re-computation as users move or enter/leave the network, and they do not directly capture area-oriented objectives (e.g., region-wide coverage and hotspot service) that evolve on a longer time scale. This talk presents an environment-aware, network-level design framework for GPA systems that shifts the objective from serving specific instantaneous users to shaping area coverage and hotspot service over longer time scales. In particular, we consider two representative settings: traffic-aware and geometry-aware. In the traffic-aware case, we model spatial demand using slowly varying hotspot profiles and optimize the activation/positioning of pinching points to align the radiated energy with traffic intensity, together with resource allocation to balance hotspot performance across the service region. In the geometry-aware case, we incorporate site-map information such as obstacles and visibility constraints into the network metric, and optimize pinching-point deployment to mitigate blockage-induced coverage holes. For each setting, we formulate a network-level optimization problem, develop low-complexity structure-exploiting algorithms, and quantify the performance–overhead tradeoff associated with antenna reconfiguration. Simulation results demonstrate that environment-aware GPA design can substantially improve region-wide coverage and hotspot service quality compared to fixed-aperture baselines and user-driven link-level heuristics, especially in obstructed environments or highly non-uniform traffic scenarios.

Biography: Yanqing Xu received the Ph.D. degree in communication and information system from the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China, in 2019. He was a senior engineer with Huawei Technologies Company Ltd., from July 2019 to July 2020. From September 2020 to August 2022, he was a PostDoc researcher with The Chinese University of Hong Kong, Shenzhen, where he is currently working as a research assistant professor. He is also with the Shenzhen Research Institute of Big Data. Dr. Xu’s current research interest lies in distributed signal processing algorithm designs for large-scale antenna systems. Dr. Xu served as a special session co-organizer and chair in IEEE SPAWC 2024. He was a recipient of the Shenzhen Overseas High-Caliber Personnel, and the Top 3% Paper Recognition of the IEEE ICASSP 2023. Several of his research outcomes have been successfully deployed in Huawei’s base stations, for which he has received the Huawei Technical Cooperation Achievement Transformation Award (1st Prize) in 2024, the Huawei Wireless Product Line Outstanding Technical Cooperation Project Award in 2024, and the Huawei Technical Cooperation Achievement Transformation Award (2nd Prize) in 2022. He is currently serving on the Editorial Board of EURASIP Journal on Wireless Communications and Networking.

 

Senior Engineer Kaikai Liu

Chongqing University of Posts and Telecommunications, China

Speech Title: Millimeter-Wave ISAC for Millimetre-Level Deformation Monitoring and Landslide Early Warning

Abstract: A narrowband millimeter-wave ISAC system is proposed for micro-deformation monitoring and landslide early warning. Unlike wideband radars, the proposed system operates with only ~200 MHz bandwidth yet achieves sub-millimeter precision (~0.5 mm @ 100 m) through advanced OFDM carrier-phase estimation. A robust signal processing pipeline that includes CSI correction, coarse displacement estimation, and fine interferometric recovery with atmospheric phase compensation overcomes the centimeter-level limit of traditional narrowband systems. Based on spatio-temporal displacement maps, a dual-CNN model is employed to perform risk classification for early warning. The proposed system is 5G-compatible, low-cost, and scalable, enabling real-time monitoring in landslides, mining, and critical infrastructure where detecting millimeter-level deformation can prevent catastrophic failures.

Biography: Kaikai Liu is a Doctoral Supervisor and Senior Engineer at Chongqing University of Posts and Telecommunications (CQUPT), China. He obtained his doctoral degree from CQUPT.
With long-term dedication to the field of mobile communications, he has solid industry-academia-research integration experience. He previously held positions at Datang Mobile Communications Equipment Co., Ltd. and China Mobile Group Design Institute Co., Ltd. Chongqing Branch. His current research interests focus on wireless sensing, integrated communication and sensing, and the Internet of Things (IoT).
He has led a series of research projects, including one General Program of the National Natural Science Foundation of China, one Key Program of the Natural Science Foundation of Chongqing, and multiple horizontal projects. He has published over 40 papers indexed by SCI/EI, been granted multiple invention patents, and participated in the formulation of 2 group standards.

 

Dr. Feng Wang
Singapore University of Technology and Design, Singapore

Speech Title: Non-Terrestrial Networking: Evolution, Opportunities, and Future Directions

Abstract: From 5G onward, Non-Terrestrial Networks have emerged as a foundational element of future global networking infrastructures. Powered by large-scale Low Earth Orbit satellite constellations, NTNs are transforming satellites from isolated communication platforms into a fully connected space Internet, enabling seamless coverage across land, sea, air, and remote regions. However, building such a highly dynamic, large-scale satellite network introduces profound networking challenges. The rapid movement of LEO satellites, frequent topology changes, and heterogeneous service demands fundamentally reshape how NTN connectivity should be designed. In this speech, I will discuss NTN evolution from a networking perspective, highlighting key mechanisms in radio resource management, mobility-aware networking, and dynamic network slicing for 6G systems. Finally, I will outline open research challenges and future directions toward scalable, resilient, and intelligent NTN architectures.

Biography: Feng Wang received the B.S. and Ph.D. degrees from University of Electronic Science and Technology of China (UESTC) in 2016 and 2022, respectively. He is currently a Research Fellow with Information Systems Technology and Design Pillar at the Singapore University of Technology and Design (SUTD), Singapore. His research interests include non-terrestrial networking (NTN), satellite mobility management, and NTN service orchestration. He was a keynote speaker at SIMUtools 2020 and a tutorial speaker at ICCT 2024, 2025. He received the Best Paper Award at SIMUtools 2019. He has orgnized multiple NTN-related symposia and workshops at international conferences. He served as a Guest Editor of Electronics and is currently a member of Youth Editorial Board of Journal of Information and Intelligence (JII).

 

Dr. Xiangyi Chen
Southwest Jiaotong University, China

Speech Title: Model Migration in Digital Twin-Empowered Vehicular Edge Computing

Abstract: The accuracy of digital twin models hinges on the prompt collection of information from the vehicular environment. However, the high mobility of vehicles and the dynamically changing network environment pose significant challenges. Dynamic twin model migration can reduce the Age of Information (AoI) by bringing twin models closer to their vehicles. Existing works rarely consider the inherent differences in optimization cycles between digital twin model migration and data upload, which potentially leads to suboptimal cost efficiency and information freshness. Specifically, real-time vehicular data must be rapidly uploaded to edge servers to ensure the accuracy and timeliness of digital twin models, while frequent migration of twin models over short periods incurs substantial costs. Therefore, we propose a dual-timescale bilevel learning approach, where the upper-layer learning optimizes twin model migration decisions on a long timescale to achieve forward-looking model migration, and the lower-layer learning optimizes data upload and resource allocation decisions on a short timescale to ensure the accuracy and timeliness of digital twin models. Then, we design a multi-agent selective parameter sharing approach based on spatiotemporal dependency correlations to accelerate model convergence and reduce communication costs among agents. Moreover, through a rigorous theoretical analysis, we prove the convergence of the dual-timescale bilevel learning with broad applicability, and extensive experimental results demonstrate the effectiveness of the proposed approach.

Biography: Her research interests include multi-access edge computing, edge intelligence, and deep reinforcement learning. She has published numerous SCI-indexed journal and conference papers in high-impact venues such as IEEE Journal on Selected Areas in Communications (JSAC), IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Computers (TC), IEEE Transactions on Services Computing (TSC), IEEE Transactions on Network Science and Engineering (TNSE), IEEE Internet of Things Journal (IoTJ), IEEE Systems Journal (SJ), and IEEE Global Communications Conference (GLOBECOM).
She has led several research projects, including the National Natural Science Foundation of China, the China Postdoctoral Science Foundation, the Natural Science Foundation of Sichuan Province, and the Fundamental Research Funds for the Central Universities. She also participates in major national-level programs such as the National Key R&D Program of China and the National Natural Science Foundation of China (NSFC).
She serves as a TPC member for the 23rd IEEE International Conference on Ubiquitous Computing and Communications (IUCC 2024 Workshop) and acts as a reviewer for leading international journals, including IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Wireless Communications (TWC), IEEE Transactions on Network Science and Engineering (TNSE), IEEE Transactions on Vehicular Technology (TVT), and IEEE Network.

 

Asst. Prof. Xiaoyi Wang
Sichuan University of Media and Communications, China

Speech Title: Design and Implementation of an LLM-Based Prompt Chain Framework for Automated Econometric Analysis: A Case Study in Corporate Sustainability

Abstract: Large Language Models (LLMs) are accelerating the democratization of complex data analytics, yet deploying them for rigorous econometric tasks remains a significant challenge due to computational inconsistencies. This study proposes and implements a novel, automated analytical framework based on Prompt Chain Engineering. Utilizing corporate sustainability data (ESG and green innovation) as a complex, high-dimensional testbed, we design a systematic, multi-stage prompt workflow. This pipeline automates data preprocessing, statistical computation, and complex quantitative modeling without relying on traditional econometric programming. The established framework explicitly defines operational pipelines, context-aware prompt templates, and reasoning constraints. To rigorously evaluate the system's reliability, we benchmark the LLM-generated outputs against standard Stata software results. Experimental validation confirms strong alignment in core metrics, including coefficient estimation, significance levels, and robustness checks, thereby demonstrating the framework's computational accuracy. Furthermore, the system extends traditional analytical boundaries by integrating an automated semantic generation module that translates numerical outputs into professional economic interpretations. This dual capability—statistical consistency paired with automated interpretive depth—proves that structured prompt engineering can effectively transform LLMs into highly reliable, accessible, and end-to-end analytical engines for complex domain data.

Biography: Xiaoyi Wang is an Assistant Professor at the School of Communication, Economics & Management, Sichuan University of Media and Communications. She earned her Bachelor's degree in Financial Management from Sichuan Normal University in 2017, followed by a Master's in Agricultural Management from Sichuan Agricultural University in 2022, and a Ph.D. in Management from Angeles University Foundation in 2024. Her research focuses on applying artificial intelligence and machine learning to risk prediction and financial investment, including related areas like AI tool implementation and prompt chain design. Dr. Wang has led and participated in multiple research projects at the provincial and municipal levels in Sichuan, and has contributed to several textbook compilations. Additionally, she serves as a reviewer for international academic journals.