Program
Tutorial
Tutorial 1
“Deep Learning Aided Intelligent Sensing and Identification for Secure Wireless Communications“
Abstract:
With the rapid development in artificial intelligence (AI) and deep learning (DL), it can be foreseen that the future wireless communication systems will have much more intelligence and secure than the predecessors. For problems that can be accurately modeled, traditional algorithms show good performance and efficient solutions on partially convex problems. However, for some non-convex problems, existing algorithms usually obtain more efficient solutions while allowing a certain performance loss. At this time, the DL technology is used to mine the parameter information of the known structure algorithm from the obtained data samples, to improve the convergence speed of the algorithm and the performance of the algorithm. In this talk, the artificial neural networks (ANNs) including deep neural network (DNN), convolutional neural network (CNN) and so on are used to parameterize the model or algorithm, and the gradient based methods are used to optimize the NNs. These methods that obtain model or algorithm features from massive amounts of data rather than based on pre-established rules are generally called data-driven. Here, this talk focuses on the research and application of DL in physical layer. On the one hand, model based algorithms for signal detection or channel estimation can be enhanced by DL to improve the computing efficiency and system performance. On the other hand, traditional model-based methods are increasingly unable to meet the increasing demands of next-generation communication systems under the channel conditions with more complex interference and higher uncertainty. DL has the potential opportunities to redesign the baseband module including coding/decoding, detection and so on.
With the rapid development in artificial intelligence (AI) and deep learning (DL), it can be foreseen that the future wireless communication systems will have much more intelligence and secure than the predecessors. For problems that can be accurately modeled, traditional algorithms show good performance and efficient solutions on partially convex problems. However, for some non-convex problems, existing algorithms usually obtain more efficient solutions while allowing a certain performance loss. At this time, the DL technology is used to mine the parameter information of the known structure algorithm from the obtained data samples, to improve the convergence speed of the algorithm and the performance of the algorithm. In this talk, the artificial neural networks (ANNs) including deep neural network (DNN), convolutional neural network (CNN) and so on are used to parameterize the model or algorithm, and the gradient based methods are used to optimize the NNs. These methods that obtain model or algorithm features from massive amounts of data rather than based on pre-established rules are generally called data-driven. Here, this talk focuses on the research and application of DL in physical layer. On the one hand, model based algorithms for signal detection or channel estimation can be enhanced by DL to improve the computing efficiency and system performance. On the other hand, traditional model-based methods are increasingly unable to meet the increasing demands of next-generation communication systems under the channel conditions with more complex interference and higher uncertainty. DL has the potential opportunities to redesign the baseband module including coding/decoding, detection and so on.
Prof. Tomoaki Ohtsuki
Biography:
Tomoaki Otsuki (Ohtsuki) received the B.E., M.E., and Ph. D. degrees in Electrical Engineering from Keio University, Yokohama, Japan in 1990, 1992, and 1994, respectively. He is now a Professor at Keio University. He has published more than 235 journal papers and 460 international conference papers. He served as a Chair of IEEE Communications Society, Signal Processing for Communications and Electronics Technical Committee. He served as a technical editor of the IEEE Wireless Communications Magazine and an editor of Elsevier Physical Communications. He is now serving as an Area Editor of the IEEE Transactions on Vehicular Technology and an editor of the IEEE Communications Surveys and Tutorials. He has served as general-co chair, symposium co-chair, and TPC co-chair of many conferences, including IEEE GLOBECOM 2008, SPC, IEEE ICC 2011, CTS, IEEE GLOBECOM 2012, SPC, IEEE ICC 2020, SPC, IEEE APWCS, IEEE SPAWC, and IEEE VTC. He gave tutorials and keynote speeches at many international conferences including IEEE VTC, IEEE PIMRC, IEEE WCNC, and so on. He was Vice President and President of the Communications Society of the IEICE. He is a senior member and a distinguished lecturer of the IEEE, a fellow of the IEICE, and a member of the Engineering Academy of Japan.
Tomoaki Otsuki (Ohtsuki) received the B.E., M.E., and Ph. D. degrees in Electrical Engineering from Keio University, Yokohama, Japan in 1990, 1992, and 1994, respectively. He is now a Professor at Keio University. He has published more than 235 journal papers and 460 international conference papers. He served as a Chair of IEEE Communications Society, Signal Processing for Communications and Electronics Technical Committee. He served as a technical editor of the IEEE Wireless Communications Magazine and an editor of Elsevier Physical Communications. He is now serving as an Area Editor of the IEEE Transactions on Vehicular Technology and an editor of the IEEE Communications Surveys and Tutorials. He has served as general-co chair, symposium co-chair, and TPC co-chair of many conferences, including IEEE GLOBECOM 2008, SPC, IEEE ICC 2011, CTS, IEEE GLOBECOM 2012, SPC, IEEE ICC 2020, SPC, IEEE APWCS, IEEE SPAWC, and IEEE VTC. He gave tutorials and keynote speeches at many international conferences including IEEE VTC, IEEE PIMRC, IEEE WCNC, and so on. He was Vice President and President of the Communications Society of the IEICE. He is a senior member and a distinguished lecturer of the IEEE, a fellow of the IEICE, and a member of the Engineering Academy of Japan.
Prof. Guan Gui
Biography:
Guan Gui was born in Zongyang county, Anhui province, China, in 1982. He received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. From 2009 to 2014, he joined the Tohoku University as a research assistant as well as a postdoctoral research fellow, respectively. From 2014 to 2015, he was an Assistant Professor in the Akita Prefectural University, Akita, Japan. Since 2015, he has been a professor with Nanjing University of Posts and Telecommunications, Nanjing, China. His recent research interests include intelligence sensing and recognition, intelligent signal processing, and physical layer security. Dr. Gui has published more than 200 IEEE Journal/Conference papers and won several best paper awards, e.g., ICC 2017, ICC 2014 and VTC 2014-Spring. He received the IEEE Communications Society Heinrich Hertz Award in 2021, top 2% scientists of the world by Stanford University in 2021, the Clarivate Analytics Highly Cited Researcher in Cross-Field in 2021, the Highly Cited Chinese Researchers by Elsevier in 2020 and 2021, the Member and Global Activities Contributions Award in 2018, the Top Editor Award of IEEE Transactions on Vehicular Technology in 2019, the Outstanding Journal Service Award of KSII Transactions on Internet and Information System in 2020, the Exemplary Reviewer Award of IEEE Communications Letters in 2017, the 2012 Japan Society for Promotion of Science (JSPS) Postdoctoral Fellowships for Foreign Researchers, and the 2018 Japan Society for Promotion of Science (JSPS) International Fellowships for Overseas Researchers. He was also selected as for the Jiangsu Specially-Appointed Professor in 2016, the Jiangsu High-level Innovation and Entrepreneurial Talent in 2016, the Jiangsu Six Top Talent in 2018. Since 2022, he has been a Distinguished Lecturer of the IEEE Vehicular Technology Society. He is a Senior Member of the IEEE, a Member of the IEEE Communications Society and of the IEEE Vehicular Technology Society. He is serving or served on the editorial boards of several journals, including IEEE Transactions on Vehicular Technology, IEICE Transactions on Communications, Physical Communication, Wireless Networks, IEEE Access, Journal of Circuits Systems and Computers, Security and Communication Networks, IEICE Communications Express, and KSII Transactions on Internet and Information Systems, Journal on Communications. In addition, he served as the IEEE VTS Ad Hoc Committee Member in AI Wireless, TPC Chair of PRAI 2022, TPC Chair of ICGIP 2022, Executive Chair of VTC 2021-Fall, Vice Chair of WCNC 2021, TPC Chair of PHM 2021, Symposium Chair of WCSP 2021, General Co-Chair of Mobimedia 2020, TPC Chair of WiMob 2020, Track Chairs of EuCNC 2021 and 2022, VTC 2020 Spring, Award Chair of PIMRC 2019, and TPC member of many IEEE international conferences, including GLOBECOM, ICC, WCNC, PIRMC, VTC, and SPAWC.
Guan Gui was born in Zongyang county, Anhui province, China, in 1982. He received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. From 2009 to 2014, he joined the Tohoku University as a research assistant as well as a postdoctoral research fellow, respectively. From 2014 to 2015, he was an Assistant Professor in the Akita Prefectural University, Akita, Japan. Since 2015, he has been a professor with Nanjing University of Posts and Telecommunications, Nanjing, China. His recent research interests include intelligence sensing and recognition, intelligent signal processing, and physical layer security. Dr. Gui has published more than 200 IEEE Journal/Conference papers and won several best paper awards, e.g., ICC 2017, ICC 2014 and VTC 2014-Spring. He received the IEEE Communications Society Heinrich Hertz Award in 2021, top 2% scientists of the world by Stanford University in 2021, the Clarivate Analytics Highly Cited Researcher in Cross-Field in 2021, the Highly Cited Chinese Researchers by Elsevier in 2020 and 2021, the Member and Global Activities Contributions Award in 2018, the Top Editor Award of IEEE Transactions on Vehicular Technology in 2019, the Outstanding Journal Service Award of KSII Transactions on Internet and Information System in 2020, the Exemplary Reviewer Award of IEEE Communications Letters in 2017, the 2012 Japan Society for Promotion of Science (JSPS) Postdoctoral Fellowships for Foreign Researchers, and the 2018 Japan Society for Promotion of Science (JSPS) International Fellowships for Overseas Researchers. He was also selected as for the Jiangsu Specially-Appointed Professor in 2016, the Jiangsu High-level Innovation and Entrepreneurial Talent in 2016, the Jiangsu Six Top Talent in 2018. Since 2022, he has been a Distinguished Lecturer of the IEEE Vehicular Technology Society. He is a Senior Member of the IEEE, a Member of the IEEE Communications Society and of the IEEE Vehicular Technology Society. He is serving or served on the editorial boards of several journals, including IEEE Transactions on Vehicular Technology, IEICE Transactions on Communications, Physical Communication, Wireless Networks, IEEE Access, Journal of Circuits Systems and Computers, Security and Communication Networks, IEICE Communications Express, and KSII Transactions on Internet and Information Systems, Journal on Communications. In addition, he served as the IEEE VTS Ad Hoc Committee Member in AI Wireless, TPC Chair of PRAI 2022, TPC Chair of ICGIP 2022, Executive Chair of VTC 2021-Fall, Vice Chair of WCNC 2021, TPC Chair of PHM 2021, Symposium Chair of WCSP 2021, General Co-Chair of Mobimedia 2020, TPC Chair of WiMob 2020, Track Chairs of EuCNC 2021 and 2022, VTC 2020 Spring, Award Chair of PIMRC 2019, and TPC member of many IEEE international conferences, including GLOBECOM, ICC, WCNC, PIRMC, VTC, and SPAWC.
Tutorial 2
Computer Vision (CV)-aided Wireless Communication for 6G
Prof. Byonghyo Shim
Seoul National Univ.
Abstract:
Recently, we are now witnessing the emergence of unprecedented services and applications using artificial intelligence (AI) such as the autonomous vehicles, drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, to name just a few. AI-based approaches are data-driven in nature, so applications using visual and audio/speech data are popular among others. In particular, computer vision (CV) technique, a field of AI that enables computers to derive meaningful information from visual data such as image and video, has achieved a remarkable success in various tasks such as the image classification, object detection, image captioning, and saliency detection. In the perspective of future wireless systems, benefits of CV are twofold: First, physical characteristics of wireless signals (in particular, mmWave and THz radio waves) are very close to the sensing signal (e.g., visible light in 400∼790 THz) in that the transmit energy is mostly concentrated in the line of sight (LoS) path. Second, recent advances of the CV techniques have made a gigantic improvement in various tasks, from which we can infer that the CV techniques can dramatically reduce the complicated control process of the wireless communication systems since the essential operation of CV-aided wireless systems is to capture the image and use AI in performing the desired task. In this tutorial, we present CV-aided future wireless systems equipped with the visual sensing mechanism (e.g., RGB, LiDAR, laser, infrared). After discussing basics of sensing devices and deep learning (DL) mechanism, we will explain the state-of-the-art CV-techniques and its applications to 6G wireless communication systems. We will also discuss the practical issues such as multi-modal sensor fusion, dataset acquisition, model training, and integration with communication systems. From our discussion, we will show that the CV technique is effective in improving the reliability and capacity, reducing the end-to-end latency and power consumption, and also operation cost of wireless systems.
Recently, we are now witnessing the emergence of unprecedented services and applications using artificial intelligence (AI) such as the autonomous vehicles, drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, to name just a few. AI-based approaches are data-driven in nature, so applications using visual and audio/speech data are popular among others. In particular, computer vision (CV) technique, a field of AI that enables computers to derive meaningful information from visual data such as image and video, has achieved a remarkable success in various tasks such as the image classification, object detection, image captioning, and saliency detection. In the perspective of future wireless systems, benefits of CV are twofold: First, physical characteristics of wireless signals (in particular, mmWave and THz radio waves) are very close to the sensing signal (e.g., visible light in 400∼790 THz) in that the transmit energy is mostly concentrated in the line of sight (LoS) path. Second, recent advances of the CV techniques have made a gigantic improvement in various tasks, from which we can infer that the CV techniques can dramatically reduce the complicated control process of the wireless communication systems since the essential operation of CV-aided wireless systems is to capture the image and use AI in performing the desired task. In this tutorial, we present CV-aided future wireless systems equipped with the visual sensing mechanism (e.g., RGB, LiDAR, laser, infrared). After discussing basics of sensing devices and deep learning (DL) mechanism, we will explain the state-of-the-art CV-techniques and its applications to 6G wireless communication systems. We will also discuss the practical issues such as multi-modal sensor fusion, dataset acquisition, model training, and integration with communication systems. From our discussion, we will show that the CV technique is effective in improving the reliability and capacity, reducing the end-to-end latency and power consumption, and also operation cost of wireless systems.
Biography:
Byonghyo Shim received the B.S. and M.S. degree in Control and Instrumentation Engineering (currently Electrical Eng.) from Seoul National University (SNU), Seoul, Korea, in 1995 and 1997, respectively, and the M.S. degree in Mathematics and the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), Urbana, in 2004 and 2005, respectively. From 1997 and 2000, he was with the Department of Electronics Engineering at the Korean Air Force Academy as an Officer (First Lieutenant) and an Academic Full-time instructor. He also had a short time research position in the Texas Instruments and Samsung Electronics in 1997 and 2004, 2019, respectively. From 2005 to 2007, he was with the Qualcomm Inc., San Diego, CA as a Staff Engineer working on CDMA systems. From 2007 to 2014, he was with the School of Information and Communication, Korea University, Seoul, Korea, as an associate professor. Since September 2014, he has been with the Dept. of Electrical and Computer Engineering, Seoul National University, where he is currently a Professor. His research interests include signal processing for wireless communications, statistical signal processing, machine learning, compressed sensing, and information theory. Dr. Shim was the recipient of the M. E. Van Valkenburg Research Award from the ECE Department of the University of Illinois (2005), the Hadong Young Engineer Award from IEIE (2010), the Irwin Jacobs Award from Qualcomm and KICS (2016), the Shinyang Research Award from the Engineering College of SNU (2017), the Okawa Foundation Research Award (2020), and the IEEE COMSOC Asia Pacific Outstanding Paper Award (2021). He was a technical committee member of Signal Processing for Communications and Networking (SPCOM), and currently serving as an associate editor of IEEE Transactions on Signal Processing (TSP), IEEE Transactions on Communications (TCOM), IEEE Transactions on Vehicular Technology (TVT), IEEE Wireless Communications Letters (WCL), Journal of Communications and Networks (JCN), and a guest editor of IEEE Journal of Selected Areas in Communications (location awareness for radios and networks).
Byonghyo Shim received the B.S. and M.S. degree in Control and Instrumentation Engineering (currently Electrical Eng.) from Seoul National University (SNU), Seoul, Korea, in 1995 and 1997, respectively, and the M.S. degree in Mathematics and the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), Urbana, in 2004 and 2005, respectively. From 1997 and 2000, he was with the Department of Electronics Engineering at the Korean Air Force Academy as an Officer (First Lieutenant) and an Academic Full-time instructor. He also had a short time research position in the Texas Instruments and Samsung Electronics in 1997 and 2004, 2019, respectively. From 2005 to 2007, he was with the Qualcomm Inc., San Diego, CA as a Staff Engineer working on CDMA systems. From 2007 to 2014, he was with the School of Information and Communication, Korea University, Seoul, Korea, as an associate professor. Since September 2014, he has been with the Dept. of Electrical and Computer Engineering, Seoul National University, where he is currently a Professor. His research interests include signal processing for wireless communications, statistical signal processing, machine learning, compressed sensing, and information theory. Dr. Shim was the recipient of the M. E. Van Valkenburg Research Award from the ECE Department of the University of Illinois (2005), the Hadong Young Engineer Award from IEIE (2010), the Irwin Jacobs Award from Qualcomm and KICS (2016), the Shinyang Research Award from the Engineering College of SNU (2017), the Okawa Foundation Research Award (2020), and the IEEE COMSOC Asia Pacific Outstanding Paper Award (2021). He was a technical committee member of Signal Processing for Communications and Networking (SPCOM), and currently serving as an associate editor of IEEE Transactions on Signal Processing (TSP), IEEE Transactions on Communications (TCOM), IEEE Transactions on Vehicular Technology (TVT), IEEE Wireless Communications Letters (WCL), Journal of Communications and Networks (JCN), and a guest editor of IEEE Journal of Selected Areas in Communications (location awareness for radios and networks).
Tutorial 3
Wireless Infrastructure of the Future: Integrated Terrestrial-HAPS-LEO Networks
Prof. Halim Yanikomeroglu
Carleton University
Abstract:
In this tutorial, we will present a multi-layer vertical access architecture composed of fully integrated terrestrial and non-terrestrial layers towards a vertical HetNet which is expected to evolve progressively in the next 20 years, during the 6G era and beyond. In particular, we will examine a new access & computing layer composed of HAPS (high altitude platform station) systems in stratosphere, 20 km above the ground, in addition to the legacy terrestrial layer and the emerging satellite layer. With its bird’s-eye and almost-line-of-sight view of an entire metropolitan area, a HAPS is more than a base station in the air; it is a new architecture paradigm with access, transport, and core network functionalities for integrated connectivity, computing, sensing, positioning, navigation, and surveillance, towards enabling a variety of use-cases in an agile, smart, and sustainable manner for smart cities and societies of the future. The tutorial will feature a number of enabling technologies for the envisioned architecture including RIS (reconfigurable intelligent surfaces) and advanced antennas.
In this tutorial, we will present a multi-layer vertical access architecture composed of fully integrated terrestrial and non-terrestrial layers towards a vertical HetNet which is expected to evolve progressively in the next 20 years, during the 6G era and beyond. In particular, we will examine a new access & computing layer composed of HAPS (high altitude platform station) systems in stratosphere, 20 km above the ground, in addition to the legacy terrestrial layer and the emerging satellite layer. With its bird’s-eye and almost-line-of-sight view of an entire metropolitan area, a HAPS is more than a base station in the air; it is a new architecture paradigm with access, transport, and core network functionalities for integrated connectivity, computing, sensing, positioning, navigation, and surveillance, towards enabling a variety of use-cases in an agile, smart, and sustainable manner for smart cities and societies of the future. The tutorial will feature a number of enabling technologies for the envisioned architecture including RIS (reconfigurable intelligent surfaces) and advanced antennas.
Biography:
Dr. Halim Yanikomeroglu is a Professor in the Department of Systems and Computer Engineering at Carleton University, Canada. His primary research domain is wireless communications and networks. His research group has made substantial contributions to 4G/5G wireless technologies. His group’s current focus is the wireless infrastructure for the 6G and B6G era with terrestrial, aerial (HAPS and UAV), and satellite network elements. He has coauthored around 550 published peer-reviewed research papers including 240+ papers in 28 different IEEE journals; these publications have received 20,000 citations. His extensive collaboration with industry resulted in 39 granted patents. Dr. Yanikomeroglu is a Fellow of IEEE, Engineering Institute of Canada (EIC), and Canadian Academy of Engineering (CAE). He is an IEEE Distinguished Speaker for ComSoc and VTS. He has given 165 keynotes, tutorials, and invited seminars in the last ten years. Dr. Yanikomeroglu is currently serving as the Steering Committee Chair of IEEE Wireless Communications and Networking Conference (WCNC). He is also a member of the IEEE ComSoc Conference Council and IEEE PIMRC Steering Committee. He served as the Honorary Chair, General Chair, and Technical Program Chair of several IEEE conferences. He has also served in the editorial boards of various IEEE periodicals. Dr. Yanikomeroglu received several awards for his research, teaching, and service, including the IEEE ComSoc Fred W. Ellersick Prize (2021), IEEE VTS Stuart Meyer Memorial Award (2020), and IEEE ComSoc Wireless Communications TC Recognition Award (2018). He received best paper awards at IEEE ICC 2021 and IEEE WISEE 2021.
Dr. Halim Yanikomeroglu is a Professor in the Department of Systems and Computer Engineering at Carleton University, Canada. His primary research domain is wireless communications and networks. His research group has made substantial contributions to 4G/5G wireless technologies. His group’s current focus is the wireless infrastructure for the 6G and B6G era with terrestrial, aerial (HAPS and UAV), and satellite network elements. He has coauthored around 550 published peer-reviewed research papers including 240+ papers in 28 different IEEE journals; these publications have received 20,000 citations. His extensive collaboration with industry resulted in 39 granted patents. Dr. Yanikomeroglu is a Fellow of IEEE, Engineering Institute of Canada (EIC), and Canadian Academy of Engineering (CAE). He is an IEEE Distinguished Speaker for ComSoc and VTS. He has given 165 keynotes, tutorials, and invited seminars in the last ten years. Dr. Yanikomeroglu is currently serving as the Steering Committee Chair of IEEE Wireless Communications and Networking Conference (WCNC). He is also a member of the IEEE ComSoc Conference Council and IEEE PIMRC Steering Committee. He served as the Honorary Chair, General Chair, and Technical Program Chair of several IEEE conferences. He has also served in the editorial boards of various IEEE periodicals. Dr. Yanikomeroglu received several awards for his research, teaching, and service, including the IEEE ComSoc Fred W. Ellersick Prize (2021), IEEE VTS Stuart Meyer Memorial Award (2020), and IEEE ComSoc Wireless Communications TC Recognition Award (2018). He received best paper awards at IEEE ICC 2021 and IEEE WISEE 2021.
Abstract:
Orthogonal time frequency space (OTFS) modulation has been recently proposed by Hadani et al. at WCNC’17, San Francisco. It was shown to offer significant advantages over OFDM in doubly dispersive channels for high mobility wireless communications. The key idea of OTFS is to model mobile wireless channels in the delay-Doppler domain, where a sparse nature of the geometry of the wireless channel is captured. This tutorial will introduce the general notion of OTFS/delay-Doppler communications, starting from the fundamentals of high mobility wireless channels, followed by the transceiver architecture used for detection and channel estimation and finally the potential application to LEO Satcom.
Orthogonal time frequency space (OTFS) modulation has been recently proposed by Hadani et al. at WCNC’17, San Francisco. It was shown to offer significant advantages over OFDM in doubly dispersive channels for high mobility wireless communications. The key idea of OTFS is to model mobile wireless channels in the delay-Doppler domain, where a sparse nature of the geometry of the wireless channel is captured. This tutorial will introduce the general notion of OTFS/delay-Doppler communications, starting from the fundamentals of high mobility wireless channels, followed by the transceiver architecture used for detection and channel estimation and finally the potential application to LEO Satcom.
Biography:
Yi Hong is an Associate Professor at the Department of Electrical and Computer Systems Engineering (ECSE), Monash University, Australia. She served as a member of the Australian Research Council (ARC) College of Experts (2018–2020) and the Director of Graduate Research in the ECSE department at Monash University (2016-18). She received the Ph.D. degree in Electrical Engineering and Telecommunications from University of New South Wales, Sydney, and she also received the NICTA-ACoRN Early Career Researcher award in AusCTW’07. She is a Fellow of IET, a Senior Member of IEEE, a member of IEEE Communications Society, IEEE Information Theory Society, and IEEE Vehicular Technology Society. She served as the Tutorial Chair of the 2021 IEEE International Symposium on Information Theory, Melbourne, the General Co-Chair of the IEEE International Conference on Communications Workshop on Orthogonal Time Frequency Space Modulation (OTFS) (2019-22). She is currently the Associate Editor (AE) of IEEE Transactions on Green Communications and Networking (TGCN), and was the AE of IEEE Wireless Communications Letters (WCL) and Transactions on Emerging Telecommunications Technologies (ETT). Her research interests include communication theory, coding, and information theory with applications to telecommunication engineering.
Yi Hong is an Associate Professor at the Department of Electrical and Computer Systems Engineering (ECSE), Monash University, Australia. She served as a member of the Australian Research Council (ARC) College of Experts (2018–2020) and the Director of Graduate Research in the ECSE department at Monash University (2016-18). She received the Ph.D. degree in Electrical Engineering and Telecommunications from University of New South Wales, Sydney, and she also received the NICTA-ACoRN Early Career Researcher award in AusCTW’07. She is a Fellow of IET, a Senior Member of IEEE, a member of IEEE Communications Society, IEEE Information Theory Society, and IEEE Vehicular Technology Society. She served as the Tutorial Chair of the 2021 IEEE International Symposium on Information Theory, Melbourne, the General Co-Chair of the IEEE International Conference on Communications Workshop on Orthogonal Time Frequency Space Modulation (OTFS) (2019-22). She is currently the Associate Editor (AE) of IEEE Transactions on Green Communications and Networking (TGCN), and was the AE of IEEE Wireless Communications Letters (WCL) and Transactions on Emerging Telecommunications Technologies (ETT). Her research interests include communication theory, coding, and information theory with applications to telecommunication engineering.