Invited Speakers of ICCSN2026
Prof. Yang Yue, Xi'an Jiaotong University, China (Fellow of Optica and SPIE)
Biography: Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is currently a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. He is the founder and current PI of Intelligent Photonic Application Technology Laboratory (iPatLab). Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published >300 journal papers (including Science) and conference proceedings with >14,000 citations, two books (Elsevier, Springer Nature), eight edited books, two book chapters, >50 issued patents (including 30 U.S. patents and 6 European patents), >200 invited presentations (including 1 tutorial, >30 plenary and >100 keynote talks). Dr. Yue is a Fellow of Optica and SPIE. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair for >100 international conferences, Reviewer for >80 prestigious journals.
Prof. Mohd Nazri Bin Ismai, National Defence University of Malaysia, Malaysia
Speech Title: Computational MRI Analysis of
Intensive Scripture Memorisation: Structural Brain Signatures among Huffaz
Abstract: Intensive verbal memorisation is a long-standing cultural and
educational practice in many societies, yet its possible association with
brain structure remains relatively underexplored. In the context of
Huffaz, individuals who have memorised the Quran, our previous work has
investigated structural brain characteristics using computational magnetic
resonance imaging (MRI) approaches, including voxel-based morphometry,
Brodmann-area-based volume of interest analysis, radiomics, fractal
analysis, texture-based feature extraction, and machine learning
classification. Across these studies, structural MRI data were analysed to
identify grey matter volume differences, regional morphological patterns,
texture-based characteristics, and radiomics profiles that may distinguish
Huffaz from non-Huffaz controls. Particular emphasis was placed on
Brodmann-area volumes of interest, where features such as grey-level
co-occurrence matrix correlation, fractal dimensions, and machine
learning-derived radiomics markers were explored as potential descriptors
of brain structural variation associated with intensive scripture
memorisation. Additional analyses also examined classification performance
across Huffaz and non-Huffaz groups, as well as sex-based classification
patterns within structural MRI data. Collectively, these findings suggest
that the sustained cognitive, linguistic, attentional, and memory-related
demands involved in memorising scripture may be associated with measurable
structural brain features. However, these results should be interpreted as
associative rather than causal, and further longitudinal, multimodal, and
larger-scale studies are needed to clarify the neurobiological mechanisms
underlying intensive verbal memorisation practices. This talk will
summarise our computational MRI findings, methodological approaches, and
future directions for neuroscience-informed research on cultural and
religious memorisation practices.
Biography: Prof. Dr. Mohd Nazri became Lecturer at
National Defence University of Malaysia. Prof. Dr. Mohd Nazri Ismail had a
deep involvement in computer network research and was awarded the
prestigious “Educator Award 2009 – R&D/Education category” by MARA
(Malaysia Agency). He has supervised Ph.D. and Master Students and
teaching at undergraduate and post graduate level. Assoc. Prof. Dr. Mohd
Nazri Ismail has published more than 100 papers in national and
international journals (indexed ISI, SCOPUS, IET) and IEEE conferences.
He has attended many international conferences throughout the world and
has chaired many technical sessions. He has appointed as Technical Program
Committee and organized more than 60 national and international
conferences. He has appointed as Editorial Board member more than 90
international journals and 40 international reviewer panels
(journal/proceeding). Awards and laurels won by Assoc. Prof. Dr. Mohd
Nazri Ismail run into volumes and he has received 28 awards in
R&D/Education. Assoc. Prof. Dr. Mohd Nazri Ismail is an International
Association of Engineers (IAENG), IEEE Cloud Computing Community, Society
of Digital Information and Wireless Communications (SDIWC), International
Association of Engineers and Scientists (IAEST), Universal Association of
Computer & Electronics Engineers (UACEE).
Biography: Paulo Batista is PhD Researcher at CIDEHUS.UÉ-Interdisciplinary Center for History, Cultures and Societies of the University of Évora, Portugal, where is the coordinator of the research group 2: Heritage and Literacies. Currently works as a higher technician in the Municipal Archives of Lisbon, and professor at the Autonomous University of Lisbon, where is coordinator and professor of the Postgraduate in Promotion and Cultural and Educational Dynamization of Archives and Libraries, and the Postgraduate in Architectural Archives. He has lectured in the MS program in Information Science and Documentation at Universidade NOVA de Lisboa and has held senior technician positions at the Portuguese Institute of Cultural Heritage, the Portuguese Institute of Architectural Heritage, and the Torre do Tombo Archives. He has also worked as researcher at the Center for the Study of History and Ancient Cartography of the Institute of Tropical Scientific Research. Paulo Batista holds a Ph.D. in Documentation (University of Alcalá, Madrid-UAH), an MS in Information Science and Documentation - Archival Studies (UNL), and an MA in Documentation (UAH). As part of his doctorate, he also received a Diploma of Advanced Studies in Bibliography and Documentation Retrospective in Humanities (UAH), and he also holds a postgraduate degree in Information Society Law (University of Lisbon) and Information and Documentation Science - Librarianship and Archival Studies (UNL), and a specialization in Good Practices in Patrimonial Management (UNL) and Information Science and Documentation - Archival Studies (UNL). He holds an undergraduate degree in History (University of Lisbon). Paulo Batista is the author of several books and about 90 papers published in international journals and conference proceedings. He was also keynote speaker and invited speaker at various international conferences (Portugal, Argentina, Belgium, Brazil, China, Ecuador, Egypt, England, Fiji, France, India, South Africa, Thailand, Türkiye and South Korea). More information: https://www.cienciavitae.pt//0618-CE7B-7145
Biography: K. VIVEKANANDA BHAT (Senior Member,
IEEE) received the Ph.D. degree in computer science and engineering from
IIT Kharagpur, India, and the M.Tech. degree in systems analysis and
computer applications from the National Institute of Technology Karnataka,
Surathkal, India. He is currently an Additional Professor with the School
of Computer Engineering, Manipal Institute of Technology, Manipal Academy
of Higher Education, Manipal, India. He has published several articles in
reputed journals and conferences. His research interests include quantum
computing and cyber security.
https://researcher.manipal.edu/en/persons/vivekananda-k-bhat/
Prof. Dr. Mohd Zulfaezal Che Azemin, International Islamic University Malaysia, Malaysia
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.
Speech Title: Multidimensional parameter estimation
for single-channel dual-polarization time-modulated array
Abstract: The effective estimation of multidimensional parameters —
including angle, polarization state, pulse width, and repetition period —
of environmental radiation signals is a key foundation for modern
battlefield environment perception. However, conventional interferometer
or multi-beam passive reconnaissance systems have high costs, making it
difficult to deploy them widely in the field. To solve this problem, this
presentation describes a study on multidimensional parameter estimation of
environmental radiation signals, based on a novel low-cost single-channel
sampling polarization-time modulated array. This approach solves practical
issues in existing technologies, such as polarization mismatch and signal
frequency conversion, and provides a technical basis for large-scale,
low-cost battlefield electromagnetic sensing.
Biography: Ma Jiazhi is currently an associate professor and master's
supervisor at the College of Electronic Science, National University of
Defense Technology. He received the M.S. degree in 2012 and Ph.D.degree in
2017. His research interests include radar signal processing and radar
polarization anti-interference. (jzmanudt@163.com / 18570108262)
Assoc. Prof. Feng Li, Xi’an Jiaotong University, China
Biography: Dr. Feng Li is an Associate Professor and Ph.D. supervisor with the School of Information and Communications Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China, where he received the B.E. and Ph.D. degrees in information and communication engineering in 2003 and 2009, respectively. He became a faculty member of the same university since 2009. He was a postdoctoral researcher of the same university. He visited the University of Liverpool in 2013 from February to May. He received the title of excellent Master's thesis supervisor in 2018, 2020 and 2021, respectively. His research interests include wireless communications, wireless positioning, deep learning and signal processing with emphasis on cognitive radio, orthogonal frequency division multiplexing, and multiple-input-multiple-output. One of his graduated students received the best oral presentation award in ICCSN 2019. He served as technical committee member of several international academic conferences. He is a guest editor of a Special issue of the journal of Applied Sciences. He is reviewer for several journals.
Assoc. Prof. Shiying Han, Nankai University, China
Biography: Dr Shiying Han (Member, IEEE) received the B.Eng. degree in
communication engineering from Tianjin University, Tianjin, China, in
2008, the M.Eng. degree in communication and information system from the
Beijing University of Posts and Telecommunications, Beijing, China, in
2011, and the Ph.D. degree in electrical and electronic engineering from
Nanyang Technological University, Singapore, in 2015. She was a Research
Fellow with NTUSinBerBEST, Singapore. She is an Associate Professor and
serve as the Assistant of Dean in the College of Electronic Information
and Optics Engineering, Nankai University. Her current research interests
focus on the intelligent spectrum sharing in heterogeneous networks,
symbiotic radio networks, ultra-lower-power wireless transmission.
Assoc. Prof. Yanqun Tang, Sun Yat-sen University, Shenzhen, China
Biography: Yanqun Tang received the B.Sc., M.Sc., and Ph.D. degrees from the School of Electronic Science and Engineering, National University of Defense Technology, China, in 2007, 2009 and 2013 respectively. He is currently an associate processor at the School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China. His research interests are integrated sensing and communication, full duplex communications, wireless physical layer security and machine learning techniques for wireless communications.
Assoc. Prof. Xiaoxuan Wang, Beijing Jiaotong University, China
Biography: Dr. Xiaoxuan Wang received his Ph.D. degree in traffic control and information engineering from Beijing Jiaotong University, Beijing, China, in 2020. From 2017 to 2018, he was a Visiting Scholar with Dr. Lingjia Liu in Wireless@VT, Virginia Tech, VA, USA. He is currently an Associate Professor at the School of Electronic and Information Engineering, Beijing Jiaotong University, and the Associate Director at the Cyber-Physical Systems & Industrial Software Lab. His main research interests are in dependability and security for wireless communications and communication networks in smart city, intelligent transportation and railway transportation. He has published more than 30 research papers in top international conferences and journals, such as IEEE T-ITS, T-VT, IoT-J, IET-ITS, ICC, WCNC, Globecom, VTC, etc. He has been the recipients of the 2018 International Conference on Intelligent Rail Transportation Best Paper Award.
Assoc. Prof. Qianli Wang, Southwest Jiaotong University, China
Speech Title: Integrated Sensing and Communication (ISAC) for Out-of-Range Multi-targets
Abstract: In integrated sensing and communication (ISAC) systems, high-mobility scenarios or long-range targets often cause the round-trip delay or Doppler shift to exceed the conventional unambiguous limits of OTFS waveforms. This not only causes ambiguous sensing results and unpredictable channels, but also exacerbates the deterministic‑random trade-off in ISAC: reliable sensing demands significantly more time‑frequency resources, which conflicts with communication efficiency. This talk presents a novel cross-frame OTFS framework that significantly extends the sensing range and channel estimation capability under out-of-range conditions. To address these issues, this talk presents a novel cross‑frame OTFS framework based on the Chinese Remainder Theorem (CRT). By designing multiple sub-frames with co-prime numbers of subcarriers and time slots, the framework exponentially extends the maximum unambiguous delay and Doppler ranges at the cost of a linear reduction in resolution. I will introduce the framework from the perspective of sensing (target detection and estimation) and communication (channel estimation). Furthermore, this talk will discuss the framework designing for muti-targets scenarios.
Biography: Qianli Wang received the B.S. degree in electronic and information engineering, M.S. degree in electronics and communication engineering and the Ph.D. degree in information and communications engineering from University of Electronic Science and Technology of China, in 2013, 2016 and 2020, respectively. Now he is an associate professor in the School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China. His research interests include estimation and detection theory, radar, array and communication signal processing, integrated sensing and communication. He has authored/co-authored over 30 journal and conference papers, including flagship journals such as IEEE TSP, IEEE TWC, IEEE TAES, IEEE TVT and flagship conferences such as IEEE GLOBECOM, IEEE ICC and IEEE Radar Conference. He has served as field TPC member of GLOBECOM 2025, 2026 and ICC 2026. He has also served as reviewer for many top journals, such as IEEE TSP, IEEE TWC, IEEE TGRS, IEEE TCOM, IEEE TVT, etc.
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. Di Zhang, Sun Yat-sen University, Shenzhen, China
Speech Title: Secured Short Packet xURLLC Technologies for 6G Mission-Critical Applications
Abstract: Sixth-generation (6G) mission-critical applications such as low altitude economy, cooperative intelligent transportation systems (C-ITS), and industrial control impose unprecedented hyper-reliable and low-latency communications (xURLLC) requirements on short packet communications.
In this talk, we present a comprehensive suite of secured and xURLLC short packet communication technologies tailored for 6G mission-critical applications. We first introduce the precise channel estimation and tracking method to enable accurate and efficient channel state information (CSI) acquisitions. Then we introduce the channel coding schemes, and finally, we introduce endogenous security mechanisms to address security threats. We hope this talk can provide some theoretical and engineering insights for 6G mission-critical applications.
Biography: Di Zhang is a Senior Member of the Chinese Institute of Command and Control, the China Institute of Communications, the Chinese Institute of Electronics and IEEE. He has received the Second Prize of the 2025 Natural Science Award of Henan Province; the Second Prize of the 2024 Science and Technology Progress Award of Henan Province; the First Prize of the 2022 Science and Technology Progress Award of Henan Province; and the Second Prize of the 2021 Sichuan-Chongqing Science and Technology Conference Award, and the 2019 ITU Young Author Recognition. His primary research interests lie in short packet communications and applications.
Assoc. Prof. Syed Mushhad Mustuzhar Gilani, University of Agriculture, Pakistan
Speech Title: Recent Trends in Cotton Disease Detection
Using Generative Adversarial Networks
Abstract: Cotton is crucial for economic growth, the production of natural
fibers, and the textile industry. Agriculture, the foundation of
development, depends largely on cotton. However, crop diseases that impact
yield, quality, and overall productivity pose serious difficulties to
cotton production. This research presents a hybrid deep learning system
to overcome the drawbacks of the traditional approaches. High-quality
synthetic cotton leaf images are produced using a Deep Convolutional GAN
(DCGAN), which improves dataset diversity and addresses the problem of
inadequate training samples. In order to better comprehend illness
classes, K-means++ clustering is also used as an optional step to
investigate organic groups inside the feature space. The ResNet50 model, a
50-layer deep CNN, is utilized to extract characteristics from
preprocessed, increased contrast images of both healthy and diseased
leaves. The classification of illnesses is improved when DCGAN is combined
with clustering. The model's robustness for cotton disease prediction is
demonstrated by experimental findings, which surpass traditional machine
learning techniques with an accuracy of 89.5%. The presented work aims to
contribute to the development of reliable, scalable, and AI-driven
solutions for sustainable cotton production and agricultural innovation.
Biography: Dr. Syed Mushhad Mustuzhar Gilani is an Associate Professor in the Department of Computer Science at the University of Agriculture, Faisalabad, Pakistan. He also serves as an Associate Senior Tutor, Postgraduate Research Advisor, and Convener of several institutional and academic committees, contributing actively to academic leadership, curriculum development, and quality assurance. Previously, Dr. Gilani served as an Assistant Professor in Computer Science and Postgraduate Research Advisor at PMAS-Arid Agriculture University, Rawalpindi, where he played a key role in strengthening postgraduate research culture, mentoring young researchers, and enhancing academic standards. Dr. Gilani earned his PhD in Computer Science from the School of Computer Science, Chongqing University of Posts and Telecommunications, China. He has an extensive research profile with extensive publications in well-reputed international journals and conferences, reflecting both theoretical depth and practical relevance. He has successfully supervised numerous MS and PhD research projects and remains actively engaged with the international academic community. Dr. Gilani has served as a keynote speaker, invited speaker, and session chair at multiple national and international conferences. He also contributes as a reviewer and technical committee member for high-impact journals and prestigious conferences, supporting rigorous peer review and scholarly dissemination.
Assoc. Prof. Derrick Boateng, Nanfang College Guangzhou, China
Speech Title: Performance Comparison of Feature-Based and Deep Learning Models for Real-Time American Sign Language Recognition
Abstract: Automatic sign language recognition is an important component in advancing assistive communication technologies and natural human–computer interaction. This paper presents a comparative study of three representative pipelines for American Sign Language (ASL) alphabet recognition, including a landmark-based machine learning approach, a traditional gradient-based method, and a deep learning architecture. The landmark-based framework utilizes MediaPipe to extract three-dimensional hand keypoints, which are classified using a Random Forest model. The second approach employs Histogram of Oriented Gradients (HOG) features combined with a Support Vector Machine (SVM) classifier, while the third approach adopts a ResNet18 convolutional neural network with edge-enhanced input. Experimental results demonstrate that the landmark-based approach achieves the highest accuracy of 97.33% while maintaining real-time efficiency. In comparison, the ResNet18 model achieves 94.48% accuracy and the HOG+SVM model achieves 93.69%. The findings indicate that structured landmark-based representations provide an effective balance between computational efficiency and recognition performance, making them well-suited for real-time gesture recognition systems.
Biography: Dr. Derrick Boateng is an Associate Professor at the School of Engineering, Nanfang College Guangzhou, with multidisciplinary expertise in artificial intelligence and intelligent optical imaging systems. Before joining Nanfang College, he completed postdoctoral research at Shenzhen University, focusing on the integration of AI with flexible sensing materials for gesture recognition and human health monitoring. He earned his Ph.D. in Information and Communication Engineering from the University of Science and Technology of China (USTC), where he developed deep learning-based methods for optical information extraction and analysis. He also holds an M.Sc. in Biomedical Engineering from Shanghai Jiao Tong University (SJTU). Dr. Boateng has published several high-impact papers in top-tier SCI-indexed journals. His research interests include AI applications, machine and deep learning, medical image processing, intelligent signal processing, and optics.
Assoc. Prof. Wei Huang, Ocean University of China, China
Speech Title: Intelligent Sensing of Ocean Sound Speed Field and Its application on Anchor Calibration and Deployment
Abstract: The ocean sound velocity field is the fundamental factor that constrains underwater acoustic positioning, communication, and navigation, and plays a crucial role in the construction of the "transparent ocean" 3D observation network and underwater positioning, navigation, and timing (PNT) system. We have studied three key issues for the development of underwater PNT systems: intelligent perception of ocean sound velocity field, optimization of deployment of anchor reference nodes, and topology planning of anchor nodes in large-scale underwater networks. We propose a SA-MDF-CNN model based on multi-source data fusion to address the drawbacks of traditional sound velocity field reconstruction methods, such as high cost, limited coverage, and low accuracy. This model combines convolutional neural networks and attention mechanisms to integrate sea surface remote sensing temperature, Argo observation data, geographic coordinates, and EOF features, fully exploring the spatiotemporal correlation of data. The experimental results verified that the proposed model has lower RMSE and better performance than SOM, traditional CNN, and spatial interpolation algorithms. On this basis, combined with the theory of acoustic refraction and Cram é r-Rao lower bound (CRLB), a ranging error model was derived, and the optimal elevation and azimuth angles of the surface reference node were solved to improve the calibration accuracy of the seabed anchor. A hybrid optimization model was established for large-scale AUV navigation scenarios to balance positioning accuracy and communication throughput. We further derived stable navigation constraints and deployment scaling rules, and analyzed the impact of cluster size and inter cluster distance on global navigation errors. Corresponding cluster deployment strategies have been proposed to adapt to different service requirements. The research results can provide theoretical basis and technical reference for the development of ocean 3D observation networks, underwater PNT systems, and large-scale underwater sensor networks.
Biography: Wei Huang, IEEE Member, associate professor and master supervisor in the Faculty of Information Science and Engineering, Ocean University of China (2022-now). He graduated from the School of Electronic Information and Communication Engineering at Wuhan University with a bachelor's degree and a doctoral degree in 2014 and 2021, respectively. His research interests are primarily focused on underwater sound speed field construction and prediction, underwater communication and localization, underwater signal processing. He have published more than 40 SCI/EI papers, including 23 SCI papers as first/corresponding author. He has been granted by 13 Chinese invention patents. He was awarded the Best Paper Award and Excellent Poster Presentation Award at the 2026 IEEE WCCCT International Conference, the Excellent Poster Presentation Award at the 2025 IEEE ICCSN International Conference, and the Best Paper Award at the 2024 SPCT International Conference. He served as a reviewer for many journals such as IEEE JOE, TCCN, TGCN, IOTJ, ESWA, EAAI, Applied Ocean Research, Ocean Engineering, etc. He also served as a publication co-chair of 2026 WCCCT, a poster session chair of 2025 ICCSN, and and a TPC member for GLOBECOM 2024 Workshop ISCEI (Workshop on Integrated Sensing and Communications for Edge Intelligence).
Assoc. Prof. Chao Fang, Beijing University of Technology, China
Speech Title: Collaborative Allocation and Intelligent Optimization of Service-Driven Cloud Radio Access Network Resources
Abstract: In order to meet the service requirements of the emerging applications such as extended reality, 8K ultra-high definition video transmission and industrial Internet of Things in terms of massive user access, heterogeneous mobile traffic processing, ultra-low latency, ultra-high reliability and other aspects, cloud-edge collaboration, as the core of cloud radio access networks (C-RAN), has been increasingly concerned and risen to the height of national development strategy. At present, the problem on cooperative allocation and optimization of cloud-edge-end resources in C-RAN is still in the initial research stage, lacking systematic and in-depth research, which makes it difficult to adaptively guarantee the differentiated service requirements of network business. Therefore, by sorting out and referring to the research ideas and methods related to cloud computing and fog computing, and drawing on future network concepts such as "separation of control and forwarding" in software-defined networking and "in-network caching" in information-centric networking, the project focuses on collaborative allocation and intelligent optimization mechanisms of service-driven C-RAN resources from the
perspective of cross-layer and cross-domain cooperation. To improve the overall service capacity and satisfy the differentiated service requirements of massive applications, key technologies such as multi-user-oriented cross-layer collaboration allocation and intelligent optimization of cloud-edge-terminal resources, multi-business-oriented cross-layer collaboration and intelligent resource allocation, multi-business-oriented cross-domain collaboration and intelligent resource allocation will be solved in C-RAN environments, providing customized service for network applications.
Biography: Chao Fang received his B.S degree in Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and the Ph.D. degree with the State Key Laboratory of Networking and Switching Technology in Information and Communication Engineering from Beijing University of Posts and Te4lecommunications, Beijing, China, in 2015. He joined the Beijing University of Technology in 2016 and now is an associate professor. From August 2013 to August 2014, he had been funded by China Scholarship Council to visit Carleton University, Ottawa, ON, Canada, as a joint doctorate. Moreover, he is the visiting scholars of University of Technology Sydney, Commonwealth Scientific and Industrial Research Organization, Hong Kong Polytechnic University, Kyoto University, Muroran Institute of Technology, and Queen Mary University of London.
Dr. Fang is the senior member of IEEE, and the vice chair of technical affairs committee in IEEE ComSoc Asia/Pacific Region (2022-2023). Moreover, he served as the Technical Program Committee Chair of SPCNC 2024, the Session Chairs of ICC 2015, ICCC 2023, and WCNC 2024, Workshop Chairs of ICFEICT (2022-2024) and ICNCIC (2023-2024), and Poster Co-Chair of HotICN 2018. He won the Best Paper Award of IEEE ICFEICT 2022 and 2024, ICCSN 2024, NCIC 2024, and ICCC 2025. His current research interests include intelligent analysis and control, and intelligent cloud-edge-terminal cooperation computing.
Assoc. Prof. Han Wu, Sichuan University, China
Biography: Han Wu received the PhD degree from the Key Lab of Optical Fiber Sensing & Communications (Education Ministry of China), University of Electronic Science & Technology of China, Chengdu, China. He was with Tampere University, Finland, as a joint-training PhD student from 2018 to 2019. He is now an associate professor at Sichuan University. He has authored or co-authored over 70 papers in international journals, including Nature Communications, Light: Science and Applications, Optica and Laser Photonics Reviews, and his research was highlighted in Optics in 2014 by OSA Optics and Photonics News. His Google Scholar citations are more than 3900 with a H-index of 35. He won the SPIE Optics and photonics education scholarship and Young Scientist Award in Advanced Fiber Laser Conference.
Associate Researcher Xinjiang Xia, Purple Mountain Laboratories, China
Speech Title: Joint Uplink-Downlink Resource Allocation for URLLC in Cell-Free RAN with Network-Assisted Free-Duplex
Abstract: Cell-free radio access network (CF-RAN) with network-assisted free-duplex (NA-FD) architecture evolved from cell-free massive MIMO (CF-mMIMO) networks. It unifies flexible duplex, hybrid duplex, and full-duplex operation to support ultra-reliable and low-latency communication (URLLC). In this architecture, uplink and downlink users remain half-duplex, while each distributed access point (AP) flexibly act as a transmit or receive unit on a per-slot basis, achieving a network-level degree of freedom for duplex. With the goal of maximizing the weighted URLLC sum-rate under finite-blocklength (FBL) reliability constraints, per-AP transmit power budgets, and edge distributed unit (EDU) information constraints, we formulate a highly non-convex mixed-integer problem coupling cross-link interference and binary duplex indicators. To tackle it, we propose a nested solution framework in which a greedy outer loop iteratively selects the duplex mode of each AP. In each greedy evaluation, a decentralized successive convex approximation (SCA) method updates UL power and DL beamforming in a distributed manner across EDUs, pushing the bulk of computation to the edge and relieving the cloud computing center (CCU). A partial inter-EDU information-sharing mechanism is adopted to curb signaling overhead, which can balance resource cost against uplink performance. Simulation results verify that the proposed greedy and SCA NA-FD strategy provides significantly higher spectral efficiency than time-division duplexing (TDD) and co-frequency co-time full-duplex (CCFD) baselines, and the adaptive duplex mode switching clearly outperforms a fixed NA-FD configuration.
Biography: Xinjiang Xia received the B.S. and M.S. degrees in communication and information systems from Hohai University, Nanjing, China, in 2009 and 2012, respectively, and the Ph.D. degree with the National Mobile Communications Research Laboratory, Southeast University, Nanjing, in 2021. He is currently an Associate Researcher with the Purple Mountain Laboratories. From 2021 to 2024, he was a Postdoctoral Scholar with the Purple Mountain Laboratories, Nanjing. His research interests include cell free massive MIMO, signal processing, and full duplex.
Dr. Dongdong Wang, the 54th Research Institute of China Electronics Technology Group Corporation (CETC), China
Speech Title: Toward GNSS-Resilient NR-NTN: Standardization Progress, Key Technical Issues, and Research Outlook
Abstract: Existing New Radio non-terrestrial network (NR-NTN) rely to varying degrees on user equipment (UE) GNSS-derived position and time information, satellite ephemeris, and network assistance for initial access, uplink timing and frequency pre-compensation, connected-mode synchronization, beam management, and mobility procedures. When GNSS signals are blocked, jammed, spoofed, or otherwise unable to provide sufficiently reliable position and time information, the resulting challenges extend beyond any single receiver algorithm. They form a cross-layer problem involving signal processing, protocol procedures, beam scheduling, state estimation, integrity assurance, and standards compatibility.
This invited talk will review the standardization evolution of 3GPP NR-NTN and clarify the roles and operating boundaries of GNSS-related assistance in current systems. It will then discuss the fundamental research questions arising when GNSS information is degraded, unavailable, or untrusted. Particular attention will be given to the common theoretical foundations underlying different technical approaches, including system-state and uncertainty representation, delay and Doppler observations, observability, synchronization holdover, beam coverage and service windows, protocol timing, and engineering feasibility constraints. Representative fixed-sweeping and steerable-beam scenarios will also be considered to examine how beam motion and scheduling affect initial acquisition, random access, connected-mode tracking, mobility management, positioning, and integrity monitoring. Finally, the talk will summarize key open issues, evaluation methodologies, and possible research directions, providing a reference for future algorithm development, system-level simulation, protocol design, and engineering validation.
Biography: Dongdong Wang, Senior Engineer and Master’s Supervisor, received his Doctor of Engineering degree in Communication and Information System from BUPT in June 2018. He conducted postdoctoral research at the 54th Research Institute of China Electronics Technology Group Corporation (CETC) from November 2020 to December 2023. Currently, he serves as an institute-level expert specializing in LEO satellite transmission technology at the Academy for Network & Communications of CETC, and Group Leader of the LEO Satellite Internet Research Group at the National Key Laboratory of Advanced Communication Networks. He has authored or co-authored more than 30 papers in international journals and conferences. His research interests cover information theory, channel coding, and 5G-based LEO satellite transmission technologies.
Biography: Changsheng You (Member, IEEE) received the B.Eng. degree from the University of Science and Technology of China (USTC) in 2014 and the Ph.D. degree from The University of Hong Kong (HKU) in 2018. He was a Research Fellow at the National University of Singapore (NUS). He is currently an Assistant Professor with the Southern University of Science and Technology. His research interests include near-field communications, intelligent reflecting surfaces, UAV communications, edge computing, and learning. He received the IEEE ComSoc Leonard G. Abraham Prize in 2025, IEEE ComSoc Best Tutorial Paper Award in 2023，IEEE ComSoc Best Survey Paper Award in 2021, and the IEEE ComSoc Asia–Pacific Region Outstanding Paper Award in 2019. He was a Clarivate Highly Cited Researcher in 2024-2025 and the IEEE ComSoc Asia-Pacific Best Yong Research Award in 2024. He is an Editor of IEEE TRANSACTIONS ONWIRELESS COMMUNICATIONS, IEEE TRANSACTIONS ON MOBILE COM-PUTING, IEEE COMMUNICATIONS LETTERS, IEEE TRANSACTIONS ONGREEN COMMUNICATIONS AND NETWORKING, and IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY.