Join us on Thursday, June 24, 2021 for the IEEE VDL: Learning to Learn to Communicate, presented by Prof. Osvaldo Simeone. Contact: IEEE Kingston ComSoc Abstract: The application of supervised learning techniques for the design of the physical layer of a communication link is often impaired by the limited amount of pilot data available for each device; while the use of unsupervised learning is typically limited by the need to carry out a large number of training iterations. In this talk, meta-learning, or learning-to-learn, is introduced as a tool to alleviate these problems. The talk will consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols over a fading channel. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. To tackle this problem, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including MAML, FOMAML, REPTILE, and CAVIA. Both offline and online solutions are developed. Speaker(s): Prof. Osvaldo Simeone Biography: Osvaldo Simeone is a Professor of Information Engineering with the Centre for Telecommunications Research at the Department of Engineering of King's College London, where he directs the King's Communications, Learning and Information Processing lab. He received an M.Sc. degree (with honors) and a Ph.D. degree in information engineering from Politecnico di Milano, Milan, Italy, in 2001 and 2005, respectively. From 2006 to 2017, he was a faculty member of the Electrical and Computer Engineering (ECE) Department at New Jersey Institute of Technology (NJIT), where he was affiliated with the Center for Wireless Information Processing (CWiP). His research interests include information theory, machine learning, wireless communications, and neuromorphic computing. Dr Simeone is a co-recipient of the 2019 IEEE Communication Society Best Tutorial Paper Award, the 2018 IEEE Signal Processing Best Paper Award, the 2017 JCN Best Paper Award, the 2015 IEEE Communication Society Best Tutorial Paper Award and of the Best Paper Awards of IEEE SPAWC 2007 and IEEE WRECOM 2007. He was awarded a Consolidator grant by the European Research Council (ERC) in 2016. His research has been supported by the U.S. NSF, the ERC, the Vienna Science and Technology Fund, as well as by a number of industrial collaborations. He currently serves in the editorial board of the IEEE Signal Processing Magazine and is the vice-chair of the Signal Processing for Communications and Networking Technical Committee of the IEEE Signal Processing Society. He was a Distinguished Lecturer of the IEEE Information Theory Society in 2017 and 2018. Dr Simeone is a co-author of two monographs, two edited books published by Cambridge University Press, and more than one hundred research journal papers. He is a Fellow of the IET and of the IEEE.

On Monday, June 28, 2021, come listen to Dr. Shiwen Mao present the IEEE ComSoc VDL: Machine Learning for Wireless Communications and Networking: Motivations, Case Studies, and Open Problems. ZOOM link will be provided to attendees. Contact: IEEE Denver ComSoc Abstract: While 5G deployment is being carried out in many places of the world, there has been great interest in the prospects of 5G beyond and the next generation. Among the various visions, a common theme is that artificial intelligence will play a key role, as evidenced by the great interest and advances in machine learning enabled wireless communications and networking. In this talk, we will discuss the motivation, potential, and challenges of incorporating machine learning in wireless communications and networking for 5G and beyond systems. We will start with two motivating examples, i.e., channel estimation and mobile edge computing, to show why machine learning could be helpful. We will share our experience of several case studies, including (i) a hybrid approach to the classical energy efficiency maximization problem, where traditional models could be used to train a deep learning model; (ii) data augmentation for convolutional neural network (CNN) based automatic modulation classification (AMC), where a conditional generative adversarial network (CGAN) is utilized to generate synthesized training data; and (iii) and an adaptive model for RFID-based 3D human skeleton tracking, which utilizes meta-learning and few-shot fine-tuning to achieve high adaptability to new environments. We will conclude this talk with a discussion of challenges and open problems. Speaker(s): Dr. Shiwen Mao Biography: Shiwen Mao received his Ph.D. in electrical engineering from Polytechnic University, Brooklyn, NY in 2004. He was a postdoc at Virginia Tech from 2004 to 2006, and joined Auburn University, Auburn, AL as an assistant professor of Electrical and Computer Engineering in 2006. He held the McWane Endowed Professorship from 2012 to 2015 and the Samuel Ginn Endowed Professorship from 2015 to 2020. Currently, he is a professor and Earle C. Williams Eminent Scholar Chair, and Director of the Wireless Engineering Research and Education Center at Auburn University. His research interest includes wireless networks, multimedia communications, and smart grid. He is on the editorial board of several IEEE and ACM journals. He is a Distinguished Lecturer of IEEE Communications Society and IEEE Council of RFID, and a Distinguished Speaker of IEEE Vehicular Technology Society. He received the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019 and NSF CAREER Award in 2010. He is a co-recipient of the 2021 IEEE Communications Society Outstanding Paper Award and the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award. Agenda: 6pm (MT) - Introductions 6:10-7:15 - VDL Presentation 7:15-7:30 - Q&A

Dr Arslan has graciously offered to continue the discussion on 5G technologies. The topic is Flexible Radio Access Beyond 5G: A Future Projection. Abstract: Today's wireless services and systems have come a long way since the rollout of the conventional voice-centric cellular systems. The demand for wireless access in voice and multi-media applications has increased tremendously. The trend on the variety and the number of mobile devices along with the mobile applications will certainly continue beyond 5G, creating a wide range of technical challenges. One of the biggest challenges is the security of the communication beyond the classical crypto based approaches which secure the information. In this talk, security aspects of the physical communication and also physical signal which is called Physical Layer Security (PHY Security) will be discussed. Latest trends, threats, and techniques to improve the security of the physical signal will be discussed. The tentative outline of the talk will be as follows: Wireless Communication trends, requirements Importance of secure communication Classification of communication security PHY security: Communication and REM PHY security: Eavesdropping, Spoofing & Jamming Anti-jamming capable communication Cross-layer security - Secure communication & other advanced radio access technologies Case- studies a- Security in URLLC (URLL & Secure communication) b- Security in vehicular network (V2V and V2I) c- Security in NOMA d- Security in LIS Speaker(s): Dr. Arslan Arslan