Enhancing Network Resilience via Detecting, Recovering, and Preventing Configuration-Induced Failures

Speaker: Wei Ni

Affiliation: School of Engineering, Edith Cowan University, Perth, WA, Australia

Abstract: Modern networks are evolving into unified terrestrial, aerial, and satellite infrastructures under 6G, making sovereign network functions increasingly prone to configuration-induced failures that can cascade into network-wide outages. This talk presents a closed-loop Detect-Recover-Prevent framework for network resilience against such failures, addressing partial observability, inadequate automatability, and limited generalizability. By jointly modelling physical network entities and logical protocol states as a bipartite graph, a graph structural inconsistency detector localizes root causes through adaptive configuration encoding and inconsistency-aware attention, exposing subtle anomalies overlooked by rule-based methods. Building on this detection capability, interpretable agentic decision-making enables automated, risk-aware recovery, while ongoing work targets anticipation of unseen failure modes. Validated on emulated testbeds, the results demonstrate the potential of graph-based resilience as a key enabler of dependable network operation in 6G.

Biography: Wei Ni (Fellow, IEEE) received the B.E. and Ph.D. degrees in electronic engineering from Fudan University, Shanghai, China, in 2000 and 2005, respectively. He is the Associate Dean (Research) with the School of Engineering, Edith Cowan University, Perth; an Adjunct Professor with the University of Technology Sydney; and a Technical Expert with Standards Australia. He was the Deputy Project Manager with Alcatel/Alcatel-Lucent Bell Labs from 2005 to 2008; a Senior Research Engineer with Nokia from 2008 to 2009; and a Senior Principal Research Scientist and Group Leader with the Commonwealth Scientific and Industrial Research Organization from 2009 to 2025. His research interests include distributed and trusted learning with constrained resources. He was a co-recipient of the ACM Conference on Computer and Communications Security (CCS) 2025 Distinguished Paper Award and four best paper awards. He was the Chair of the IEEE VTS NSW Chapter (2020–2022), the Workshop Co-Chair for ICC 2027, the PC Co-Chair for MSN 2026, the Track Chair for VTC-Spring 2017, the Track Co-Chair for IEEE VTC-Spring 2016, the Publication Chair for BodyNet 2015, and the Student Travel Grant Chair for WPMC 2014. He has been an Editor or a Senior Area Editor of IEEE Transactions on Wireless Communications since 2018, IEEE Transactions on Vehicular Technology since 2022, IEEE Transactions on Information Forensics and Security since 2024, IEEE Communication Surveys and Tutorials since 2024, IEEE Transactions on Network Science and Engineering since 2025, and IEEE Transactions on Cloud Computing since 2025.

Reciprocity-Based Bistatic ISAC in 6G

Speaker: Jinho Choi

Affiliation: University of Adelaide, Australia

Abstract:

6G networks and beyond are expected to integrate sensing and communication to enable environment-aware wireless systems. This talk presents a reciprocity-based bistatic ISAC framework that exploits the reciprocal nature of wireless propagation to perform cooperative sensing without requiring tight synchronization among distributed nodes.

Through the exchange of sensing roles between transmitters and receivers, the proposed approach improves target resolvability and localization accuracy by leveraging complementary observations. Furthermore, by exploiting reciprocity in propagation delays, target pairing methods are developed to distinguish real targets from ghost targets. An analytical framework is also presented to characterize pairing errors and reveal the impact of sensing geometry and delay estimation accuracy. The results demonstrate the potential of reciprocity as a key enabler of synchronization-free distributed sensing in future 6G networks.

Biography:

Jinho Choi was born in Seoul, Korea. He received the B.E. degree, magna cum laude, in electronics engineering from Sogang University, Seoul, in 1989, and the M.S.E. and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology, in 1991 and 1994, respectively. He is a Professor with the School of Electrical and Mechanical Engineering, University of Adelaide, Australia.

His research interests include the Internet of Things, wireless communications, and statistical signal processing. He authored two books published by Cambridge University Press in 2006 and 2010 and one book by Wiley-IEEE in 2022. Professor Choi received a number of best paper awards, including the 1999 Best Paper Award for Signal Processing from EURASIP.

He is a Fellow of the IEEE and has been listed among the World’s Top 2% Scientists by Stanford University since 2020. He is currently a Senior Editor of IEEE Wireless Communications Letters and an Associate Editor of IEEE Transactions on Mobile Computing. He has also served as a Division Editor of the Journal of Communications and Networks and as an Associate Editor or Editor of several journals, including IEEE Transactions on Communications, IEEE Communications Letters, Journal of Communications and Networks, IEEE Transactions on Vehicular Technology, and ETRI Journal.

Semantic Sensing: A Task-Oriented Paradigm

Speaker: J. Andrew Zhang

Affiliation: School of Electrical and Data Engineering, Global BigData Technologies Centre, University of Technology Sydney, Sydney, Australia

Abstract:

The emergence of semantic communications has shifted communication system design from delivering bits to delivering meaning. A similar transformation is now needed in sensing. Conventional sensing focuses on estimating physical parameters such as distance, velocity, and angle, whereas many emerging applications ultimately require semantic information, such as object categories, activities, and environmental understanding. This motivates semantic sensing, a new task-oriented sensing paradigm that directly extracts high-level semantic information from wireless observations. By shifting the objective from parameter reconstruction to task effectiveness, semantic sensing enables sensing resources to be focused on acquiring the most relevant information for the intended task.

In this talk, I will first introduce the concept of semantic sensing and discuss its relationship to semantic communications, ISAC, and conventional sensing. I will then present an information-theoretic framework based on the information bottleneck principle, together with a semantic sensing architecture and an end-to-end optimization framework that jointly designs waveform sensing and semantic inference. A practical OFDM-based implementation for target classification will be used to demonstrate the concept. Finally, I will present representative results and discuss future directions toward semantic ISAC and intelligent wireless sensing systems.

Biography:

Dr J. Andrew Zhang (M'04-SM'11) is a Professor in the School of Electrical and Data Engineering, University of Technology Sydney, Australia. His research interests are in the area of signal processing for wireless communications and sensing. Prof. Zhang has published more than 300 papers in leading Journals and conference proceedings, and has won 7 best paper awards. He is a recipient of the CSIRO Chairman's Medal and the Australian Engineering Innovation Award for exceptional research achievements in multi-gigabit wireless communications.

Prof. Zhang is one of the pioneering researchers in ISAC. He initiated the concept of perceptive mobile networks in 2017. Since then, his team has published 80+ top-tier journal papers on ISAC, including several highly cited and review articles. In this field, he has led or participated in multiple research projects with a total value of over AUD 8 million, co-established a Joint Laboratory on Network Sensing with a mobile network operator, developed multiple real-time ISAC demonstration systems, and is currently advancing their commercialization. For details, please refer to https://sites.google.com/view/andrewzhang/.

Terahertz Communications: Unlocking the Wireless Frontier for 6G and Beyond

Speaker: Nan Yang

Affiliation: School of Engineering, The Australian National University, Canberra, ACT, Australia

Abstract: As the wireless landscape evolves toward 6G and beyond, the Terahertz (THz) band (0.1–10 THz) has emerged as a pivotal frontier with the potential to overcome spectrum scarcity and transcend current capacity limits. This spectral expansion is essential for supporting the transformative applications of the 2030s, such as multi-terabit-per-second backhaul, holographic communications, and immersive extended reality (XR), which demand unprecedented quality of service. In this talk, the speaker will explore the critical role of THz communications in next-generation wireless networks, providing an overview of fundamental research in THz channel properties and emerging standardisation. Moreover, the speaker will highlight recent advances in THz communications, specifically focusing on performance analysis and signal processing. The presentation concludes by addressing the pressing technical challenges that remain in operationalising THz systems for the 6G and beyond era.

Biography: Prof. Nan Yang received the Ph.D. degree from Beijing Institute of Technology, China, in March 2011. Since July 2014, he has been with the Australian National University, Canberra, Australia, where he is currently a Professor, the Lead of the Information and Signal Processing Cluster, and the head of the Emerging Communications Laboratory in the School of Engineering. He is an IEEE Vehicular Technology Society Distinguished Lecturer (2025-2027) and was an IEEE ComSoc Distinguished Lecturer (2023-2024). He received the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2014, and the Best Paper Awards from IEEE ComSoc SPCC-TC 2024, IEEE ICC 2024, IEEE GlobeCOM 2022, IEEE GlobeCOM 2016 and IEEE VTC 2013-Spring. He is serving on the Editorial Board of the IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, IEEE Open Journal of the Communications Society, and the IEEE Communications Letters, and was serving on the Editorial Board of the IEEE Transactions on Wireless Communications and the IEEE Transactions on Vehicular Technology. His research interests include terahertz communications, intelligent communications, ultra-reliable and low-latency communications, cyber-physical security, and molecular communications.

Critical-proof Communication for Future-facing Industries

Speaker: Raheeb Muzaffar

Affiliation: Silicon Austria Labs GmbH, Austria

Abstract:

Reliable, resilient, and deterministic communication are key requirements for industrial automation, robotics, and autonomous platforms, which represent a mission critical class of cyber-physical systems. 5G and emerging 6G communication technologies are expected to support applications that require capabilities beyond traditional ultra-reliable low-latency communication. Future industrial environments will require tight integration of communication, sensing, computation, and control. The close interaction between physical systems and their digital twins can enable predictive operation and optimized resource allocation in complex industrial environments.

In this talk, we discuss automation of industrial tasks through collaborative robots and reliable wireless communication, highlighting how digital twins enable task planning and scheduling across operational technology applications and communication networks. Specifically, we examine wireless integration of Frame Replication and Elimination for Reliability (FRER) as a mechanism to improve determinism, fault tolerance, and service continuity. Together, deterministic communication, integrated sensing, and digital twin technology lay the foundation for resilient, self-optimizing 6G networks capable of dependable and dynamic industrial environments.

Biography: Dr. Raheeb Muzaffar has more than 15 years of experience in the field of communication engineering and information technology both in the academic and industrial sectors. He is currently working as a senior scientist in the wireless communication research unit at Silicon Austria Labs. He received the M.S. degree in information technology from the National University of Sciences and Technology, Pakistan in 2006 and a Doctorate degree in 2016 in the field of Electronic Engineering from the University of Klagenfurt, Austria and Queen Mary University of London, United Kingdom under the Erasmus Mundus joint Doctorate programme specializing in multimedia communication in drone networks. From 2016 to 2020, he worked as a researcher with Lakeside Labs GmbH, Austria where he was involved in several projects related to drone navigation and communication using WiFi and 4G/5G cellular systems. His current work focuses on the research and development of 5G/6G communication for the industrial internet of things, wireless time-sensitive networking, and communication functional safety. Post his M.S. degree he gained more than 6 years of professional experience with national and multinational organizations in IT, telecom, network, system, and web administration. His technical expertise extends to system analysis, design, quality assurance, and capacity building. He has authored several research articles published in prestigious journals and conferences and has been involved in several national and international research projects.

Your Neural Network Changes With the Channel: What Can We Do?

Speaker: Jingge Zhu

Affiliation: Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia

Abstract:

In edge learning, neural networks (NNs) are partitioned across distributed edge devices that collaboratively perform inference through wireless transmission. In this setting, the wireless channel inadvertently becomes part of the effective machine learning model. This creates two unique challenges: variations in the wireless channel lead to variations in the effective neural network, and the exact channel realizations encountered during inference are unknown at the training stage.

In this talk, we establish a theoretical framework for evaluating the performance of such systems with provable guarantees. The resulting analysis further motivates a channel-aware training algorithm for robust edge inference over varying wireless channels. Joint work with Yangshuo He and Guanding Yu.

Biography: Jingge Zhu (Member, IEEE) received the B.S. and M.S. degrees in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, the Dipl.-Ing. degree in technische informatik from Technische Universität Berlin, Berlin, Germany, and the Doctorat ès Sciences degree from École Poly-technique Fédérale (ÉPFL), Lausanne, Switzerland. He was a Post-Doctoral Researcher with the University of California, Berkeley. He is currently a Senior Lecturer with The University of Melbourne, Australia. His research interests include information theory with applications in communication systems and machine learning. He received the Discovery Early Career Research Award (DECRA) from Australian Research Council in 2021, the IEEE Heinrich Hertz Award for Best Communications Letters in 2013, the Early Post-Doctoral Mobility Fellowship from the Swiss National Science Foundation in 2015, and Chinese Government Award for Outstanding Students Abroad in 2016.

Beyond Black-Box Intelligence: Integrating Mathematical Models, Zero Trust, and AI in 6G

Speaker: Mukhtar Hussain

Affiliation: Charles Darwin University, Australia

Abstract:

The evolution toward 6G is driving communications networks from connectivity platforms to intelligent cyber-physical ecosystems capable of autonomous decision-making. While recent advances in artificial intelligence, foundation models, and agentic systems promise unprecedented adaptability, purely data-driven approaches often lack explainability, robustness, and trustworthiness in safety-critical environments.

The future 6G architectures must combine mathematically grounded system models with AI-driven intelligence to achieve reliable, secure, and autonomous operation. Mathematical models provide structural knowledge, constraints, and verifiable guarantees, while AI enables adaptation to dynamic network conditions and evolving threats. This convergence can support resilient network management, cyber threat intelligence sharing, autonomous security orchestration, and trustworthy AI-native communications, outlining key research challenges and opportunities toward the realisation of secure 6G ecosystems.

Biography: Dr. Mukhtar Hussain received the Ph.D. degree in Cyber Security from Queensland University of Technology, Australia, in 2022, the Master’s degree in Information and Communication Engineering from Xi’an Jiaotong University, China, in 2016, and the Bachelor’s degree in Electrical Engineering with a specialization in Telecommunications from COMSATS, Pakistan, in 2013. His research interests are broadly in cyber security, with particular focus on wireless communication security, zero trust architecture, and anomaly detection. He has extensive experience in academic research and industry-aligned teaching, combining strong technical expertise with a commitment to practical applications in secure and resilient communication systems.

TBD

Speaker: Xiaoxu Zhang

Affiliation: School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

Abstract: TBD

Biography: Xiaoxu Zhang (associate professor, IEEE Senior Member) received her PhD degree in communication and information systems from University of Electric Science and Technology of China (UESTC) in 2019. From August 2017 to August 2018, she was with the department of Electrical and Computer Engineering, McGill University, as a visiting scholar. She joined the Southwest Jiaotong University (SWJTU) in 2019. Her main research areas are wireless communications, sparse signal processing, high mobility communications, Bayesian inference for data analysis, machine learning, and artificial intelligence. She is currently a reviewer for international journals and conference papers such as IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Wireless Communications Letters and IEEE Vehicular Technology Conference.

TBD

Speaker: Xiaoyang Li

Affiliation: Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Abstract: TBD

Biography: Xiaoyang Li (Member, IEEE) received the Ph.D. degree from The University of Hong Kong. He is currently an Assistant Professor with the Southern University of Science and Technology. His research interests include integrated sensing-communication-computation and edge learning. He was a recipient of Young Elite Scientists Sponsorship Program by CAST, Forbes China 30 under 30, Young Elite of G20, Overseas Youth Talent in Guangdong, Overseas High-Caliber Personnel in Shenzhen, Outstanding Research Fellow in Shenzhen, and the Best Paper Award of Fourth IEEE International Symposium on Joint Communications & Sensing; and the Exemplary Reviewers of IEEE Wireless Communications Letters and Journal of Information and Intelligence. He has served as an Editor for JII, and the Workshop Chairs for IEEE ICASSP/WCNC/PIMRC/MIIS/MediCom.