Prof. Pascal Lorenz
University of Haute-Alsace, France

Bio: Pascal Lorenz (lorenz@ieee.org) received his M.Sc. (1990) and Ph.D. (1994) from the University of Nancy, France. Between 1990 and 1995 he was a research engineer at WorldFIP Europe and at Alcatel-Alsthom. He is a professor at the University of Haute-Alsace, France, since 1995. His research interests include QoS, wireless networks and high-speed networks. He is the author/co-author of 3 books, 3 patents and 200 international publications in refereed journals and conferences. He was Technical Editor of the IEEE Communications Magazine Editorial Board (2000-2006), IEEE Networks Magazine since 2015, IEEE Transactions on Vehicular Technology since 2017, Chair of IEEE ComSoc France (2014-2020), Financial chair of IEEE France (2017-2022), Chair of Vertical Issues in Communication Systems Technical Committee Cluster (2008-2009), Chair of the Communications Systems Integration and Modeling Technical Committee (2003-2009), Chair of the Communications Software Technical Committee (2008-2010) and Chair of the Technical Committee on Information Infrastructure and Networking (2016-2017), Chair of IEEE/ComSoc Satellite and Space Communications Technical (2022-2023), IEEE R8 Finance Committee (2022-2023), IEEE R8 Conference Coordination Committee (2023). He has served as Co-Program Chair of IEEE WCNC'2012 and ICC'2004, Executive Vice-Chair of ICC'2017, TPC Vice Chair of Globecom'2018, Panel sessions co-chair for Globecom'16, tutorial chair of VTC'2013 Spring and WCNC'2010, track chair of PIMRC'2012 and WCNC'2014, symposium Co-Chair at Globecom 2007-2011, Globecom'2019, ICC 2008-2010, ICC'2014 and '2016. He has served as Co-Guest Editor for special issues of IEEE Communications Magazine, Networks Magazine, Wireless Communications Magazine, Telecommunications Systems and LNCS. He is associate Editor for International Journal of Communication Systems (IJCS-Wiley), Journal on Security and Communication Networks (SCN-Wiley) and International Journal of Business Data Communications and Networking, Journal of Network and Computer Applications (JNCA-Elsevier). He is senior member of the IEEE, IARIA fellow and member of many international program committees. He has organized many conferences, chaired several technical sessions and gave tutorials at major international conferences. He was IEEE ComSoc Distinguished Lecturer Tour during 2013-2014.

Speech Title: Advanced Architectures of Next Generation Wireless Networks

Abstract: Internet Quality of Service (QoS) mechanisms are expected to enable wide spread use of real time services. New standards and new communication architectures allowing guaranteed QoS services are now developed. We will cover the issues of QoS provisioning in heterogeneous networks, Internet access over 5G networks and discusses most emerging technologies in the area of networks and telecommunications such as IoT, SDN, Edge Computing and MEC networking. We will also present routing, security, baseline architectures of the inter-networking protocols and end-to-end traffic management issues.

 

Assoc. Prof. Chao Fang
Beijing University of Technology, China

Bio: 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 scholar 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 is the leading editors of Electronics and Symmetry special issues. He also served as the Session Chairs of ICC NGN'2015 and ICCC NMNRM'2021, and Poster Co-Chair of HotICN'2018. He won the Best Paper Award of IEEE ICFEICT'2022. His current research interests include future networks, information-centric networking (ICN), cloud-edge-terminal cooperation networks, intelligent network control, resource management and content delivery.

Speech Title: Intelligent Task Offloading for Caching-Assisted UAV Networks

Abstract: To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks in unmanned aerial vehicle (UAV) networks, it is urgent to efficiently allocate limited network resources to improve network performance. In this paper, we propose an intelligent task offloading scheme to optimize resource allocation in UAV networks with content caching. Specifically, we formulate the joint optimization of task offloading and resource allocation as a latency minimization model for the caching-assisted UAV system. Then, a new deep reinforcement learning (DRL) algorithm is designed to make offloading and resource allocation decisions based on current network state information, significantly improving resource utilization. Numerical results indicate that the model significantly reduces network latency in comparison to its existing benchmarks in caching-assisted UAV networks.

 

Assoc. Prof. Yan Lin
Nanjing University of Science and Technology, China

Bio: Yan Lin received the M.S. and Ph.D. degree from Southeast University, China, in 2013 and 2018, respectively. She visited Southampton Wireless Group in Southampton University, U.K. from Oct. 2016 to Oct. 2017. Since 2018, she has been working at Nanjing University of Science and Technology, China, in 2018, where she is currently an associate professor with the School of Electronic and Optical Engineering. She has co-authored more than 50 journals and conferences, such as IEEE JSAC/TWC/TCOM/TVT/IOT, and holds 10 Chinese patents. She has presided over and participated in several projects funded by National Natural Science Foundation of China and Natural Science Foundation of Jiangsu Province. She also has served as TPC members for several IEEE conferences, and as reviewers for many IEEE journals and conferences. Her current research interests include mobile edge computing and resource allocation in vehicular networks, and anti-jamming communication in UAV networks.

Speech Title: Improving Age of Information (AoI) in Vehicular Edge Computing Based on Multi-Agent Deep Reinforcement Learning With Attention Mechanism

Abstract: In the face of increasingly computing-intensive and delay-sensitive vehicular applications, vehicular edge computing (VEC) has emerged as a promising paradigm by deploying computing resources at the edge. This speech introduces the age of information (AoI) challenge in VEC, and presents the vehicular edge offloading problem formulation and solutions by dynamically adjusting the edge offloading ratio and the VEC server selection. Aiming for improving both AoI and computing energy efficiency (CEE), a novel cooperative edge offloading solution based on multi-agent deep reinforcement learning is proposed. To better adapt to the time-varying network topology, the actor-attention-critic framework is employed, where the importance of different levels of attention to other vehicular agents is considered in decision-making for each vehicular agent. The simulation results show that the proposed solution can achieve a more compelling trade-off between AoI and CEE compared to the baseline solutions.

 

Lecturer Xiaosi Tan
Southeast University, China

Bio: Xiaosi Tan received her B.S. degree from Beijing University of Technology, Beijing, China, in 2009, and her Ph.D. degree from Texas A&M University, College Station, TX, USA, in 2015. She was a Post-Doctoral Research Associate with Texas A&M University from 2015 to 2017. She joined the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China as a Post-Doctoral Researcher in 2017, where she is currently a lecturer. Her current research interests include efficient algorithms and VLSI architectures for B5G/6G baseband signal processing, and machine learning for wireless communications. She has co-authored more than 30 papers in journals and conferences on these subjects, and has led and participated in several projects funded by National Natural Science Foundation of China.

Speech Title: Expectation Propagation for Massive MIMO Detection: Algorithms and Implementations

Abstract: With the escalating demands for high-speed, low-latency, and wide-coverage communications, massive multiple-input multiple-output (MIMO) has emerged as a key technology for B5G and 6G wireless communication systems. However, the MIMO detection problem still poses substantial computational challenges due to the large number of antennas and users involved. Expectation Propagation (EP), a powerful inference technique rooted in Bayesian probability theory, offers an elegant solution for MIMO detection by iteratively refining approximate posterior distributions. This talk will provide a comprehensive overview of EP-based detection algorithms tailored for massive MIMO scenarios, highlighting their theoretical foundations and performance-complexity trade-offs. In addition, various EP-based joint detection and decoding techniques will be covered. Furthermore, we will discuss efficient hardware architectures and implementations of EP-based detectors.

Dr. Abhimanyu Mukerji
Amazon, USA

 

Bio: Abhimanyu is an Economist at Amazon working on dynamic causal models and causal machine learning. His prior research has used methods from machine learning, deep learning and natural language processing combined with econometric approaches to study problems in applied microeconomics and empirical corporate finance. He holds a PhD in financial economics from Stanford University.

Speech Title: Causal Inference, Machine Learning and Deep Learning

Abstract: This talk will provide an overview of the problem of causal inference in the technology industry and current approaches to address it. We will discuss the applications of machine learning and deep learning to this problem space, with inspiration from our own work. We will also touch upon the challenges involved in validating solutions and some tests that can be performed to build confidence in causal results.

Dr. Suwen Song
Sun Yat-Sen University, China

 Bio: Suwen Song received her B.S. degree in electronic information science and technology and her Ph.D. degree in electronic science and technology from Nanjing University, Nanjing, China, in 2017 and 2022, respectively. She is currently an assistant professor at Sun Yat-Sen University, Shenzhen, China. Previously she worked for Nanjing University from 2022 to 2023 as an associate researcher. Her research interests include channel coding algorithms, massive MIMO, and low-power, high-throughput VLSI systems for digital signal processing. She has published over 25 papers in mainstream journals and conferences of the IEEE, and has applied for or been granted more than 10 national invention patents. She has led and participated in several projects funded by National Natural Science Foundation of China, as well as collaborations with large enterprises and research institutes.

Speech Title: Efficient Decoder Design for Soft-Assisted Product Decoder

Abstract: Product code has been proven as an efficient choice for achieving high net coding gain (NCG) at extremely low bit error rates (BER) in fiber communication systems. Compared to the hard-decision product decoders, it has been demonstrated that decoders based on soft-assisted decoding algorithms can achieve excellent decoding performance with only a slight increase in area. This speech introduce some efficient designs for product code and its typical component codes.

Dr. Amjad Ali Amjad
Zhejiang University, China

Bio: Amjad Ali Amjad received his B.S. degree (Hons.) in Computer Systems Engineering from the University of Engineering and Technology (UET), Peshawar, Pakistan, in 2014, his M.S. degree in Electrical Engineering from the University of Lahore, Islamabad, Pakistan, in 2017, and his Ph.D. from Zhejiang University in 2021. He recently completed his first postdoctoral research at the School of Electronic and Computer Engineering at Peking University. He is currently engaged in his second postdoctoral research at the Donghai Laboratory in collaboration with Zhejiang University. His research interests include wireless optical communications, underwater wireless optical communication, solidstate lighting, and visible light communication. He has co-authored one book chapter and several papers on these subjects, published in refereed journals and conference proceedings.

Speech Title: Laser Diode-Based high speed optical wireless communication and high CRI Solid State Lighting

Abstract: Gallium nitride (GaN) phosphor-converted white light-emitting diodes (Pc-WLEDs) are emerging as an indispensable solid-state lighting (SSL) source for next-generation display systems and the lighting industry. Together with the function of lighting, visible light communication (VLC) using Pc-WLEDs has gained increasing attention to fulfill the growing demand for wireless data communication. Over the past few years, white-light-emitting diodes have been used for both high-speed visible light communication and solid-state lighting simultaneously. Practically, the low modulation response and low emitting intensity of light-emitting diodes (LED) are the drawbacks to the development of ultrahigh-speed VLC and a high-quality SSL system. Blue GaN laser diode (LD) and color convertor quantum dots-based white light can simultaneously be used for both high-speed VLC and SSL.