PAST TALKS

Title: Near-Instantaneously Adaptive Coherent/Non-Coherent Space-Air-Ground Integrated Networking Science Fiction or a Pareto-Optimal Next-Generation Wireless Enabler

Abstract:

Integrated terrestrial, UAV-aided, airplane-assisted as well as satellite-based global coverage-solutions pave the way for seamless next-generation service provision across the globe. However, these links exhibit strongly heterogeneous properties, and hence requiring near-instantaneously adaptive enabling techniques.

A particularly challenging aspect of near-instantaneously adaptive SAGIN systems is that given the extremely high Doppler frequency of satellites and airplanes acting as mobile base-stations, the pilot overhead required for channel estimation doubles every time, when the velocity is doubled. This potentially prevents the employment of coherent detection, unless specific transceivers, such as Orthogonal Time, Frequency and Space (OTFS) domain based solutions are conceived.

We conclude with the joint Pareto-optimization of the associated conflicting performance metrics of throughput, transmit power, latency, error probability, hand-over probability and link-lifetime. Sophisticated multi-component system optimization is required for finding the Pareto-front of all optimal solutions, where none of the above-mentioned metric can be improved without degrading at least one of the others.

Biography:

Lajos Hanzo is a professor at the University of Southampton, a Fellow of the Royal Academy of Engineering (FREng), FIEEE, FIET and a EURASIP Fellow, Foreign Member of the Hungarian Academy of Science. He holds honorary Doctorates from the University of Edinburgh and the Technical University of Budapest. He co-authored 19 IEEE Press-John Wiley books and 2000+ research contributions at IEEE Xplore. For further information on his research in progress and associated publications please refer to IEEE Xplore.

Title: On the Maximum Size of Block Codes Subject to a Distance Criterion

Abstract:

We establish a general formula for the maximum size of finite length block codes with minimum pairwise distance no less than d. The achievability argument involves an iterative construction of a set of radius-d balls, each centered at a codeword. We demonstrate that the number of such balls that cover the entire code space cannot exceed this maximum size. Our approach can be applied to codes i) with elements over arbitrary code alphabets, and ii) under a broad class of distance measures. Our formula indicates that the maximum code size can be fully characterized by the cumulative distribution function of the distance measure evaluated at two independent and identically distributed random codewords. When the two random codewords assume a uniform distribution over the entire code alphabet, our formula recovers and thus naturally generalizes the Gilbert-Varshamov (GV) lower bound. Finally, we extend our study to the asymptotic setting. This is joint work with Ling-Hua Chang, Po-Ning Chen, Carol Wang, and Yunghsiang S. Han.

Biography:

Vincent Y. F. Tan (S'07-M'11-SM'15) was born in Singapore in 1981. He received the B.A. and M.Eng. degrees in electrical and information science from Cambridge University in 2005, and the Ph.D. degree in electrical engineering and computer science (EECS) from the Massachusetts Institute of Technology (MIT) in 2011. He is currently a Dean’s Chair Associate Professor with the Department of Electrical and Computer Engineering and the Department of Mathematics, National University of Singapore (NUS). His research interests include information theory, machine learning, and statistical signal processing.

Dr. Tan is an elected member of the IEEE Information Theory Society Board of Governors. He was an IEEE Information Theory Society Distinguished Lecturer from 2018 to 2019. He received the MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize in 2011, the NUS Young Investigator Award in 2014, the Singapore National Research Foundation (NRF) Fellowship (Class of 2018), the Engineering Young Researcher Award in 2018, and the NUS Young Researcher Award in 2019. A dedicated educator, he was awarded the Engineering Educator Award in 2020 and 2021 and the (university level) Annual Teaching Excellence Award in 2022. He is currently serving as a Senior Area Editor for the IEEE Transactions on Signal Processing and as an Associate Editor in Machine Learning and Statistics for the IEEE Transactions on Information Theory.