KEYNOTE SPEAKERS

 

 

Speech Title: AI-enabled Self-driving Communication Networks

Abstract: Modern Artificial Intelligence (AI) has proven to be a powerful enabler that has gained success in many vertical fields. There is a clear evidence of determined effort in the communication and network community to explore the AI power to deliver 6G mobile network’s promises of being faster, greener and smarter. This talk starts with a brief introduction of 6G mobile communication systems, and then looks into how new AI technologies, and in particular machine learning, come into play in 6G from different perspectives. It covers new trends in 6G communication research such as data-driven communication system design, semantic communications, digital twin networks (DTN), and large model for wireless networks. One major objective of these researches is to achieve self-driving communication networks where lengthy standardization of such as communication waveforms or protocol design can be somehow reduced or even eliminated, thus enabling 6G to self-drive to versatile requirements from vertical industries.

Bio: Kun Yang received his PhD from the Department of Electronic & Electrical Engineering of University College London (UCL), UK. He is currently a Chair Professor in the School of Computer Science & Electronic Engineering, University of Essex, UK, leading the Network Convergence Laboratory (NCL). He is also an affiliated professor of UESTC. His main research interests include wireless networks and communications, future Internet and edge computing. In particular he is interested in energy aspects of future communication systems such as 6G, promoting energy self-sustainability via both energy efficiency (green communications and networking) and energy harvesting (wireless charging). He has managed research projects funded by UK EPSRC, EU FP7/H2020, and industries. He has published 400+ papers and filed 20 patents. He serves on the editorial boards of a number of IEEE journals (e.g., IEEE ComMag, TNSE, WCL, TVT). He is a Deputy Editor-in-Chief of IET Smart Cities Journal. He is a Distinguished Lecturer of IEEE ComSoc. He has been a Judge of GSMA GLOMO Award at World Mobile Congress – Barcelona since 2019. He is a Member of Academia Europaea (MAE), IEEE Fellow, IET Fellow, and an ACM Distinguished Scientist.

 

 

Speech Title: Unlocking the Potential of Federated Learning: A Path towards Future Network Intelligence

Abstract: Machine learning, particularly distributed learning, stands as the cornerstone in the vision of future network intelligence, owing to its remarkable capability of addressing intricate computational tasks and modeling complexities. In this talk, we provide a comprehensive coverage of a distributed learning paradigm rooted in federated learning. Specifically, we start with a brief overview of federated learning. Then, we elucidate an over-the-air computation-based variant of federated learning, which circumvents the communication bottleneck by harnessing the superposition properties of wireless channels. Notably, such a scheme presents new advantages, such as reduced processing latency and enhanced privacy protection. We also discuss several approaches to personalize the federated learning framework by addressing challenges stemming from data heterogeneity. Lastly, we share some of our recent works investigating the interplay between federated learning and foundation models.

Bio: Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, respectively. At Massachusetts Institute of Technology, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD) and ST Engineering Distinguished Professor. He also serves as the Head of ISTD Pillar, Director for Future Communications R&D Programme, Sector Lead for SUTD AI Program, and the Deputy Director of SUTD-ZJU IDEA. His current research topics include wireless communications and networking, 6G, network intelligence, non-terrestrial networks, and open radio access network.

Dr. Quek has been actively involved in organizing and chairing sessions and has served as a TPC member in numerous international conferences. He is currently serving as an Area Editor for the IEEE Transactions on Wireless Communications. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications, an Editor of the IEEE Transactions on Communications, and an Editor of the IEEE Wireless Communications Letters.

Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professorship, the 2022 IEEE Signal Processing Society Best Paper Award, and the 2021-2023 World's Top 2% Scientists. He is a Fellow of IEEE and a Fellow of the Academy of Engineering Singapore.

 

 

Speech Title: Multiobjective Evolutionary Computation based Decomposition

Abstract: Many optimization problems in the real world, by nature, have multiple conflicting objectives. Unlike a single optimization problem, multiobjective optimization problem has a set of Pareto optimal solutions (Pareto front) which are often required by a decision maker. Evolutionary algorithms are able to generate an approximation to the Pareto front in a single run, and many traditional optimization methods have been also developed for dealing with multiple objectives. Combination of evolutionary algorithms and traditional optimization methods should be a next generation multiobjective optimization solver. Decomposition techniques have been well used and studied in traditional multiobjective optimization. Over the last decade, a lot of effort has been devoted to build efficient multiobjective evolutionary algorithms based on decomposition (MOEA/D). In this talk, I will describe main ideas and techniques and some recent development in MOEA/D. I will also discuss some possible research issues in multiobjective evolutionary computation.

Bio: Qingfu Zhang is Chair Professor of Computational Intelligence at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. MOEA/D, a multiobjective optimization algorithm developed by him and his students, is one of the two most used multiobjective optimization framework. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He has been in the list of SCI highly cited researchers for five consecutive years, from 2016 to 2020. He is an IEEE fellow.

INVITED SPEAKERS

 

Speech Title: RIS-assisted Communications: Mutual-Coupling- Aware Model and System Optimization

Abstract: The performances of wireless communication systems are constrained by the quality of the channel environment. With the introduction of Reconfigurable Intelligent Surface (RIS), channel parameters become more controllable. This revolutionary transformation significantly enhances the flexibility of communication systems. Specially, RIS facilitates the adjustment of electromagnetic wave propagation paths, reducing signal transmission losses and ensuring more effective and stable signal transmission. However, most of theoretical studies often overlook its corresponding hardware characteristics (such as unit types, array configurations, coupling effects, electromagnetic properties of input waves). Indeed, it leaves room for further exploration in characterizing its physical features. In this talk, we focus on the coupling effects of RIS. Particularly, we systematically review the research background of coupling models and further develop a highly adaptable end-to-end RIS-assisted communication model. It may demonstrate the pivotal significance of its physical characteristics in theoretical analysis.

Bio: Jie Tang is a professor at South China University of Technology and vice dean of the School of Electronics and Information Technology. He is a recipient of National Science Fund for Excellent Young Scholars and the IEEE Communications Society Asia-Pacific Outstanding Young Scholar Award. He is the deputy director of the Engineering Research Center of the Ministry of Education for Near Field Communication and Networks and the director of the Engineering Research Center of Guangdong Province for Intelligent Network Communication and Computing. He has been engaged in the academic research and engineering development of wireless communications for a long time. He has published more than 100 papers in IEEE journals and received 5 awards from IEEE WCSP, IEEE ICNC and other international conferences. He has presided over more than 30 scientific projects and enterprise-commissioned projects, including the National Key R&D Program, Guangdong Province Key Areas R&D Program, etc. Some of the results have been industrialized and applied. He has been awarded six scientific and technological awards, such as the First Prize of Guangdong Province Electronic Information Science and Technology Award and the Second Prize of Guangdong Province Science and Technology Progress Award.

Personal Website: https://yanzhao.scut.edu.cn/open/ExpertInfo.aspx?zjbh=jAxeXRUecjTAjkxrmc2Dnw==

 

 

Speech Title: Secure Communication in UAV-assisted Mobile Edge Computing Networks

Abstract: Equipped with mobile edge computing (MEC) servers on UAVs, it can not only save the cost of installing physical infrastructure on the ground and overcome the limitations and shortcomings of ground edge computing, but also achieve fast and timely processing of computing tasks for terminal devices, improve user service quality, and reduce consumption of wireless resources. However, due to the broadcast characteristics of wireless communications, it is easy for malicious users to eavesdrop on the data offloaded from terminal devices to UAVs, which poses a great risk to the secure transmission of data. In this talk, I would like to discuss the data secure transmission issue in UAV-assisted MEC systems and provide a security guarantee mechanism for data offloading from the perspective of physical layer security. Through exploring the inherent connection between data offloading and data secure transmission, to bring the advantages of fast task processing brought by UAV-assisted MEC, while satisfying data security guarantee and service quality requirement.

Bio: Weidang Lu received the Ph.D. degree in Information and Communication Engineering from Harbin Institute of Technology in 2012. He was a visiting scholar with the Nanyang Technology University, Singapore, The Chinese University of Hong Kong, China and Southern University of Science and Technology, China. He is currently a Professor with the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. His current research interests include UAV communication, intelligent communication, secure communication and mobile edge computing. His works received several awards, including Zhejiang natural science award, Jiangxi natural science award, best paper awards of WiSATS 2019 and AICON 2021.

 

 

Speech Title: Exploring the Potential of Audio: The Novel Methods in Digital Health

Abstract: Computer Audition (CA) is an interdisciplinary subject that integrates acoustics, signal processing, machine learning, and deep learning. CA plays a crucial role in fields like digital medicine, smart healthcare, and bioinformatics. Audio signal has non-invasive, easily accessible, and ubiquitous characteristics by nature. Benefited from the development of artificial intelligence and wearable technology, CA has achieved a series of promising results in assisted diagnosis and early intervention of physical and psychiatric diseases. This speech will outline the opportunities and challenges in the field of CA for medicine applications from the research experience of the speaker.

Bio: Kun QIAN is a (full) Professor and PhD Supervisor at Beijing Institute of Technology (BIT). He was selected into the “National High-Level Talents (Youth Project)” and the “BIT Teli Young Fellow”. He serves as the Secretary-General of the Sound and Music Technology Committee of the China Audio Industry Association, and is listed in the “2023 Forbes China 100 Outstanding Overseas Returnee”. He is a Senior Member of the IEEE. Prof. Qian has been engaged in the research of machine learning/deep learning in medical health, audio intelligent sensing and intelligent Internet of Things. He has published more than 130 papers (among them more than 100 are as first author or corresponding author), including the prestigious academic journals such as IEEE Signal Processing Magazine, IEEE IoTJ, IEEE J-BHI, IEEET-ASE, IEEE T-BME, ABME, and JASA.

 

 

Bio: Jiagui Wu is a full Professor with the School of Physical Science and Technology, Southwest University and a visiting scholar in the University of California, Los Angeles, USA. He has authored or co-authored over 70 publications including about 50 journal papers. His research interests include information security, micro-nanophotonic, near-infrared, mid-infrared and far-infrared Technologies and Applications. Personal Web: http://physics.swu.edu.cn/info/1073/2999.htm

 

 

 

Speech Title: Physical Layer Design for Wireless Communications with Digital Twin Networks

Abstrat: Digital twin networks are constructed by digitally modeling wireless enviroments and wireless network functions. They should possess the capability of self-evolving, while matching their physical counterparts. Hence, Digital twin networks are capable of predicing realistic network enviroments, which can be further relied upon for optimizing and deploying transmission strategies in both physical and network layer. This presentation will introduce some initial thoughts on wireless physical layer design with the concept of digital twin networks. It includes data augmentation of wireless channel states, wireless channel prediction in digital twin networks. It also includes their applications in link-level beamforming design as well as in optimization of cell-free networks. Our initial results demonstrate that digital twin networks are capable of improving the performance of its physical counterpart.

Bio: Jie Hu has been working with the School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), China, as an Associate Professor since March 2016. He has been elected into UESTC’s Fundamental Research Program for Young Scientists since 2018. He also won UESTC’s Academic Young Talent Award in 2019. His research now is mainly funded by National Natural Science Foundation of China (NSFC). He is also in great partnership with industry, such as Huawei and State Grid Corporation of China. He is an associate editor for both IEEE Wireless Communications Letters and IET Smart Cities. He served for IEEE/CIC China Communications and ZTE communications as a guest editor. He is now a member of IEEE Technical Committee on Green Communications and Computing (TCGCC). He is a program vice chair for IEEE TrustCom 2020. He also serves as a technical program committee (TPC) member for several prestigious IEEE conferences, such as IEEE Globecom/ICC/WCSP and etc. He has a broad range of interests in wireless communication and networking, such as physical layer technologies for B5G/6G, wireless information and power transfer and communication and computation convergence.

 

 

 

Speech Title: Formal Verification Method for Fault Coverage Capability of March Algorithm based on SpinalHDL

Abstract: March algorithm is a series of algorithms commonly used for memory testing, aiming to detect and diagnose faults in memory. The continuously evolving March algorithms are capable of detecting an increasing number of complex faults. However, the question of whether March algorithm can achieve the intended design goal, i.e., achieving full coverage of theoretically detectable faults, currently lacks practical validation methods. One approach to address this issue is to conduct formal verification of the fault detection capability of March algorithm on a software platform. Therefore, this paper proposes a method that utilizes SpinalHDL to model the fault behavior of memory cells and March algorithm. By simulating the progression of March algorithm within the memory cells, reaching sensitization conditions during the progression, injecting faults into the memory cells, and checking whether March algorithm can detect these faults, the fault detection capability of March algorithm can be validated to determine if it meets the design expectations.

Bio: Xiao Yindong, a professor at the University of Electronic Science and Technology, has been at the forefront of integrated circuit (IC) testing system research, contributing innovative methods for test vector synthesis instruction design, optimization of complex vector synthesis scheduling, and enhancement of simulation and RF IC testing algorithms. These advancements have notably improved key performance indicators, including vector synthesis rate, test signal quality, and single-chip testing efficiency. As the principal investigator of 8 national projects, including 3 key initiatives and a National Natural Science Foundation grant, Professor Xiao has published over 20 academic papers, with more than 10 indexed in the SCI. He is a respected reviewer for the esteemed journal "ISA Transactions" and holds a US patent and 25 Chinese patents. His groundbreaking work has earned him the first prize from the Ministry of Education for Technical Invention. His development of a state-of-the-art, fully proprietary IC testing system has set a new benchmark in the industry, providing a comprehensive alternative to high-end testing instruments and meeting the urgent testing demands of chip development and production facilities. As a key contributor to the SpinalHDL agile digital design development library, Professor Xiao is pioneering research into next-generation hardware-software integrated HDL languages and their applications, further advancing the field of IC testing and design.

 

 

Speech Title: A Survey of Web-Oriented Storyline Mining

Abstract: The complex Web information makes it difficult for people to quickly and accurately obtain the storyline of news events. Therefore, “storyline mining” has become a valid research issue in recent years, with the purpose to extract the evolutionary stages of events and further explore the evolution model of events by analyzing the correlation between news events and subsequent related events. Storyline mining can be applied to many applications, such as web news retrieval, text summarization, and public opinion monitoring. This proposal first outlines the definition, process, and main tasks of storyline mining. Next, from the aspects of storyline generation and event evolution analysis, the main signs of progress of the current studies on this task are introduced in detail. Finally, several future research directions and technical frameworks for storyline mining are discussed in the proposal.

Bio: Dr. Zhao is an associate professor in the School of Computer Science and Technology, Southwest University of Science and Technology, China. He received his Ph.D. degree in computer science from the University of Science and Technology of China (USTC) in 2012. And he was a visiting scientist (guest researcher) in 2011 at the Spoken Language Systems Laboratory, Saarland University, Germany. His main research interests include Statistical Natural Language Processing, Machine Learning, Information Extraction (IE) and Web Search. In recent years, he has published more than 70 papers in journals and conferences and compiled two academic monographs. He serves in several reviewer boards for several international conferences and journals. Dr. Zhao has served as the Chair of Text Analysis Forum on WAIM/APWEB'19, PC member of WAIM/APWEB’13, WAIM/APWEB’19. Dr. Zhao is a member of IEEE and ACM, a senior member of China Computer Society (CCF), a committee member of the CCF Database Society.

 

 

Speech Title: A Green Base Station Dual Power Supply Strategy

Abstract: During the last decades, the electricity required for base stations has been a huge expense for communication operators. With the development of mobile communication technology, the intensive deployment of base stations has become a critical approach to meet the demand for high-speed data transmission. However, this will further increase operators' electricity bills. The issue of huge electricity bills has become a heavy burden for operators. The utilize of renewable energy is the key to solving this problem. Due to the instability of renewable energy sources, many power supply strategies have been proposed. Green hybrid energy dual power supply system has been recently proposed as most promising approach to address the disadvantages of renewable energy. Therefore, a solar-based dual power supply strategy is proposed to tackle the issue in this article. The strategy consists of the Grid-Connection Depth (GCD) model and the Battery Energy Sharing (BES) model. When renewable energy is insufficient, the GCD model is adopted to utilize as much renewable energy as possible. When renewable energy is abundant, the BES model is exploited to fully utilize the idle energy. Through the combination of the two models, the goal of maximizing the utilization of renewable energy at base stations can be achieved to reduce electricity bills for operators. Moreover, the both optimal energy transfer values are obtained through the optimization of two above models. The proposed strategy is numerically analyzed and compared with the other strategies. Finally, the proposed strategy is proved to be of high practical value.

Bio: Dr. Li is an associate professor in the School of Electronics and Electrical Engineering, Ningxia University, China. He received his Ph.D. degree in Information and Communication Engineering from Beijing University of Posts and Telecommunications (BUPT) in 2012. And he did Postdocal research from 2015 to 2017 at EEC, the University of Florida, USA. His main research interests include B5G/6G, Space-Air-Ground Integrated Network Architecture, Network Function Virtualization and Software Definition,Green Communication and Energy Efficiency,Integration of Communication and Energy networks,and Resource management for future communication. In recent years, he has published more than 30 papers in journals and conferences. He serves in several reviewer boards for several international conferences and journals. Dr. Li has served as the Chair of IEEE ICCT and ICCC.

 

Assoc. Prof. Dandan Li

Beijing University of Posts and Telecommunications, China

 

Bio: Dandan Li received her Ph.D. degree from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2017. She is currently an associate professor in School of Computer Science (National Pilot Software Engineering School) of BUPT. Her research interests is privacy and security issues in networking application, edge intelligence.

 

Assoc. Prof. Liwei Yang

China Agricultural University, China

 

Speech Title: Resource Allocation for SFC Networks: A Deep Reinforcement Learning Approach

Abstract: The rapid evolution of mobile communications technology has led to the widespread adoption of cutting-edge technologies such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN). Network flexibility and scalability have been greatly improved by virtualizing traditional dedicated hardware functions on standard hardware and servers. However, the effective deployment of Service Function Chains (SFC) and the allocation of computing resources in this virtualized network environment remain to be solved. This study presents a resource allocation approach using the Proximal Policy Optimization (PPO) algorithm. It features a novel SFC network model, utilizing reinforcement learning to intelligently train and make decisions, focusing on the cost-performance relationship in node deployment. Simulation results show that this algorithm effectively balances the relationship between node deployment cost and end-to-end service latency while ensuring the quality of service requirements.

Bio: 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.

 

Prof. Botao Feng

Shenzhen University, China

 

Speech Title: A Research on the Advanced 5G/6G Antenna-Taking the High-Capacity High-Gain Wide-Coverage Ceiling Antenna as an Example

Abstract: A dual-polarized (DP) antenna with a conical radiation pattern and high gain characteristics is proposed. It is mainly comprised of a horizontally polarized (HP) array, a vertically polarized (VP) element, a fence, and a feeding network. In the HP direction, a rotatable stacked substrate-integrated waveguide (SIW)-to-coaxial-to-SIW transition (SCST) is meticulously designed to yield omnidirectional radiation with low gain variations and low transmission loss. As for the VP direction, two orthogonal substrates that are arranged above the VP radiating element act as a holder to fix the director, resulting in gain enhancement. Here, the VP element is placed in the center of the HP array to share the aperture for size reduction. Moreover, the top-hat-shaped metal fence collaborates with the large-size ground plane to yield dual conical beams with high gain in DP directions. From the measured results, the HP direction has exhibited desirable bandwidth of 11.4% (24.8–27.8 GHz) with a peak gain of 11.6 dBi, and the VP direction has demonstrated wider bandwidth of 13.7% (24.5–28.1 GHz) with a corresponding gain up to 8.1 dBi. Notably, low gain variations of ±0.65 and ±0.8 dBi are also realized for the HP and VP directions, respectively. Therefore, a DP antenna with high-gain conical beams and low gain variations can be obtained for the fifth-generation (5G) millimeter-wave (MMW) ceiling communications.

Bio: Botao Feng (Senior Member, IEEE) was born in Guangdong, China, in 1980. He received the B.S. and M.S. degrees in communication engineering, and signal and information processing from the Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China, in 2004 and 2009, respectively, and the Ph.D. degree in communication and information system from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2015.
Dr. Feng joined Nokia Mobile Phones Ltd., Dongguan, China, as a Communication Engineer, in 2004. From 2009 to 2012, he served as a Senior Research Engineer and a Chief Executive in China United Network Communications Company Ltd., Guangzhou, China, where he won the Award of Breakout Star of the Year and the title of Technical Innovation Expert. Dr. Feng currently acts as the Head of the Shenzhen University Key Laboratory of Wireless Communication, Antennas and Propagation, which includes more than 50 research members and is a founding member of State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), and the President of Shenzhen Broad-Shine Technology Company Ltd., Shenzhen, China. He has authored or co-authored more than 160 science citation index (SCI) and engineering index (EI) articles and holds more than 80 technical patents. Since 2017, he has obtained the award of the Outstanding Instructor of the First Prize in the National Graduate Electronic Contest and the Tencent Outstanding Teacher Award. Recently, he has been selected as the consecutive winner of "Stanford University World's Top 2% Scientists".

 

Assoc. Prof. Zhengqiang Wang

Chongqing University of Posts and Telecommunications, China

 

Speech Title: Resource Allocation for UAV-IRS assisted NOMA-URLLC Systems

Abstract: This work focuses on optimizing the sum rate for unmanned aerial vehicle-mounted reconfigurable intelligent surface (UAV-RIS) assisted ultra-reliable low-latency communication (URLLC) systems with non-orthogonal multiple access (NOMA) protocol. The original optimization problem involves non-convex integer constraints, which makes it challenging to obtain the optimal solution. An efficient resource allocation solution is proposed by successive convex approximation, slack variables, and penalty-based methods. Simulation results demonstrate the proposed NOMA scheme has superior performance compared to the orthogonal multiple access (OMA) scheme.

Bio: Zhengqiang Wang received his Ph.D. degree in the Department of Electronic Engineering from Shanghai Jiao Tong University (SJTU) in 2015. He is currently an Associate Professor with the School of Communications and Information Engineering in Chongqing University of Posts and Telecommunications. He has been a Visiting Scholar with the Department of Electrical and Computer Engineering, National University of Singapore from September 2018 to September 2019. He has published a monograph and authored or co-authored over 80 papers in journals and international conferences in addition to 34 granted patents. He is a Senior Member of the IEEE. His current research interests include green communication, physical layer security, and network optimization for wireless communication.

 

Assoc. Prof. Sanshan Sun

Sichuan Normal University, China

 

Speech Title: Reinforcement Learning Based Collaborative Computing in Internet of Vehicles

Abstract: With the widespread commercialization of the 5G mobile communication system, the Internet of Vehicles (IoV) has seized a precious development opportunity. Relying on the high-bandwidth, ultra-reliability, and low-latency communication provided by the 5G system, the IoV caters to users with traditional multimedia interactive experiences and vigorously embarks on the development of intelligent driving computational services. There is an explosive growth in user demand for computing resources. However, the limited computing resources of local vehicles pose a challenge to the provision of computational services within the IoV. Thus, efficiently utilizing computing resources within the IoV has emerged as an exciting research focus.

Bio: Sanshan Sun has a Ph.D. in Communication and Information Systems and is an Associate Professor in the College of Physics and Electronic Engineering at Sichuan Normal University. He supervises master candidate and is the director of the Department of Communication Engineering. Additionally, he is an Executive Committee Member at the Artificial Intelligence-Blood Tumor and Cell Therapy Branch of the Sichuan Bioinformatics Society.
His research focuses primarily on intelligent communication networks, channel detection and estimation, and medical information applications of machine learning. He has published over 30 papers indexed in SCI and EI databases in renowned domestic and international journals/conferences. He has also obtained registration for more than 10 software copyrights. He serves as a reviewer for journals such as IEEE Transactions on Vehicular Technology, IEEE Access, and Wireless Networks.