Instructors
Dr. Boya Di, b.di@imperial.ac.uk
Imperial College London, UK
Boya Di (S’17-M’19) received the B.S. degree from Peking University in 2014, and the Ph.D. degree from Peking University in 2019. Currently she is post-doctoral research associate in Imperial College London, London, UK. Her main research interests include reconfigurable intelligent surfaces, multi-agent systems, wireless resource allocation and management, edge computing, and optimization theory. She has conducted over 16 journal papers on the topic of reconfigurable intelligent surface aided communications and sensing, including 2 first-author ones. One of her journal papers is currently listed as highly cited papers in Web of Science. She is an Editor for IEEE Transactions on Vehicular Technology. She has also served as a reviewer for multiple IEEE journals including IEEE JSAC, TWC, TCOM, etc., and a TPC member for IEEE GLOBECOM, ICC, and WCNC for several times.
Dr. Hongliang Zhang, hz16@princeton.edu
Princeton University, NJ, USA
Hongliang Zhang (S’15-M’19) received the B.S. and Ph.D. degrees at the School of Electrical Engineering and Computer Science at Peking University, in 2014 and 2019, respectively. He was a Postdoctoral Fellow in the Electrical and Computer Engineering Department at the University of Houston, Texas from Jul. 2019 to Jul. 2020. Currently, he is a Postdoctoral Associate in the Department of Electrical Engineering at Princeton University, New Jersey. His current research interest includes cooperative communications, Internet-of-Things networks, hypergraph theory, and optimization theory. He received the best doctoral thesis award from Chinese Institute of Electronics in 2019. He has served as a TPC Member for many IEEE conferences, such as Globecom, ICC, and WCNC. He is currently an Editor for IET Communications. He also serves as a Guest Editor for IEEE IoT-J special issue on Internet of UAVs over Cellular Networks.
Dr. Zhu Han, zhan22@gmail.com
Houston University, TX, USA
Zhu Han (S’01–M’04-SM’09-F’14) received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a Professor in Electrical and Computer Engineering Department as well as Computer Science Department at the University of Houston, Texas. His research interests include wireless resource allocation and management, wireless communications and networking, game theory, wireless multimedia, security, and smart grid communication. Dr. Han received an NSF Career Award in 2010, the Fred W. Ellersick Prize of the IEEE Communication Society in 2011, the EURASIP Best Paper Award for the Journal on Advances in Signal Processing in 2015, the IEEE Kiyo Tomiyasu Award in 2021, and several best paper awards in IEEE conferences. Dr. Han is top 1% highly cited researcher according to Web of Science since 2017, and AAAS fellow since 2019.
Dr. Lingyang Song, lingyang.song@pku.edu.cn
Peking University, Beijing, China
Lingyang Song (S’03-M’06-SM’12-F’19) received his PhD from the University of York, UK, in 2007, where he received the K. M. Stott Prize for excellent research. He worked as a research fellow at the University of Oslo, Norway until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, and is now a Boya Distinguished Professor. His main research interests include wireless communications, mobile computing, and machine learning. Dr. Song is the co-author of many awards, including IEEE Leonard G. Abraham Prize in 2016, IEEE ICC 2014, IEEE ICC 2015, IEEE Globecom 2014, and the best demo award in the ACM Mobihoc 2015. He received National Science Fund for Distinguished Young Scholars in 2017, First Prize in Nature Science Award of Ministry of Education of China in 2017. Dr. Song has served as a IEEE ComSoc Distinguished Lecturer (2015-2018), an Area Editor of IEEE Transactions on Vehicular Technology (2019-), Co-chair of IEEE Communications Society Asia Pacific Board Technical Affairs Committee (2020-). He is a Clarivate Analytics Highly Cited Researcher.
Motivation
The sixthgeneration communications are evoluting towards a distributed intelligent communication and sensing systemfor wireless networks. The applicability of existing techniques such as massive MIMO and traditional wireless sensor devices is limited by the conflicts between 1) low hardware costs and high data rates, 2) high detection accuracy and light-weight deployment. Fortunately, meta-material, as a passive two-dimensional metamaterial, provides a groundbreaking technology that enhances the sensing and directional transmission without extra hardware costs.
Meta-materialis capable to actively shape uncontrollable wireless environments into a desirable form via flexible phase shift reconfiguration. To exploit such a technique, the network is expected to coordinate the meta-materialand cellular access points to improve the data rates and cell coverage. As such, challenges have been posed to develop new communication diagrams including channel estimation and beamforming schemes.
Moreover, meta-materialprovides a new approach to achieve sensing applications with high accuracy. One typical application is radio frequency (RF) sensing where the meta-materialcustomizes the wireless channels intelligently which interact with the sensing objectives to excavate the environment information. Another application is high-resolution wireless sensors by leveraging the metamaterial’s electromagnetic property (determined by its geometry and material) sensitive to the environment. Both applications require new meta-materialstructures, protocols for data acquisition and processing, and meta-materialconfiguration optimization.
To address the research advances, a tutorial containing the basic concepts/theories that enable meta-materialaided communication and sensing will be very useful for researchers and engineers. This is the primary motivation of this tutorial proposal.
There are three main objectives of this tutorial. The first objective is to provide a general introduction of the intelligent meta-materialalong withthe state-of-the-art. The second objective is to introduce its unique featureswhich enlighten its broad applications to communication and sensing. Related design, analysis, optimization, and signal processing techniques will be presented. The third objective is to explore typical meta-materialapplications and discuss implementation issues. Formalized analysis of several up-to-date challenges and technical details on system design will be provided for different applications.
Structure and content
The tutorial proposal with the title “MetaEverything: Intelligent Meta-MaterialAided Sensing and Communications” will provide the state-of-the-art of research on meta-materialassisted sensing and communications from the perspectives of physical, MAC, network, and application layers. It focuses on three types of meta-materialbased applications, i.e., cellular communications, RF sensing, and high-resolution sensors. The tutorial will discuss the meta-materialhardware design as well as data processing techniques for different sensing applications. Technical issues related to communications will also be addressed including beamforming scheme design, channel estimation, phase shift optimization, signal detection, and MAC layer protocol design. The contents will be organized in the following way:
- Background
- An introduction to wireless communications (including channel models) and wireless sensor technologies with a potential to satisfy the requirements of 6G urging an intelligent world will be reviewed.
- The working principle, structure, historical development, and state-of-the-art applications of intelligent meta-materials will be introduced in detail. Both the reflective-type and reflective-transmissive types of meta-materials will be illustrated, along with our prototypes. Models of channel propagation, transmission, and sensing will be presented as well.
- Theoretical Fundamentals
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Optimization techniques and machine learning methods will be discussed in preparationfor the beamforming design and signal processing. Major optimization variations such as constrained optimization and combinatorial optimization will be presented. State-of-the-art learning techniques including reinforcement learning and deep learning will be investigated.
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- IntelligentMeta-Materialaided Cellular Communications
- Meta-materialaided cellular communications will be discussed where the meta-materialreflects signals from the cellular AP towards a user via its inherent analog beamforming. Theoretical analysis on achievable rates influenced by meta-surface size and phase shift discretization will be delivered. Coverage extension enabled by the meta-materialwill also be investigated, providing a guideline for system design.
- Meta-materialaided multi-user communications will be investigated. To support diverse requirements, e.g., high energy efficiency and high throughput, it is desirable to design hybrid beamforming schemes and to develop discrete phase shift optimization of the meta-material. The multi-user systems aided by the reflective-type (RIS) and the intelligent omni-directional meta-surfaces (IOS) are discussed in detail, respectively.
- IntelligentMeta-Materialaided Smart Sensing
- In smart sensing, the influence of the sensing objectives on the wireless signal propagation can be potentially recognized by the receivers, which is then utilized to identify the objectives. It is desirable to design image recovery algorithms, optimize meta-materialconfigurations, and study tracking methods. Two RIS-aided applications, i.e., posture recognition and 3-dimensional sensing, are illustrated, respectively.
- For enhanced localization, meta-materialis deployed between the AP and users such that the AP can analyze the reflected signals from users via different configurationsto obtain the accurate locations of users. To deal with the mutual influence between multiple users and the meta-material, a new localization protocol for device coordination and a meta-materialconfiguration optimization algorithm is required.
- IntelligentMeta-MaterialEnabled IoT
- The application of indoor climate monitoringis deployed based on the meta-materialhigh-resolution sensors. Unlike traditional sensors, one small piece of meta-materialcan enable an integration of multiple types of sensors which collects data simultaneously and provides the integrated temperature and humidity information with an ultra-high resolution. The goal is to develop a new meta-materialbased data collection, sensing, and processing approach that strikes a balance between the low cost and high resolution.
Based on the above mjor contents, we present the outline of the tutorial below: