Introduction To Communication Systems Upamanyu Madhow Pdf

[Lu Rounsaville]

TerahertzTHz) communications is envisioned as an up-and-coming and pivotal wireless technology for the sixth generation (6G) and beyond era. In particular, the ultra-wide THz band ranging from 0.1 to 10 THz offers enormous potential to alleviate the spectrum scarcity and break the capacity limitation of emerging wireless systems (such as 4G-LTE and 5G NR). This will undoubtedly support epoch-making wireless applications that demand ultra-high quality service requirements and multi-terabits per second data transmission in the intelligent information society in the 2030s, such as terabit-per-second backhaul systems, ultra-high-definition content streaming among mobile devices, and virtual/augmented reality. In this talk, I will introduce the role and importance of THz communications for the 6G and beyond wireless networks, while briefly describing the fundamental research results on THz channels, devices, and standardisation. After this, I will give a brief summary of recent research advances in THz communications, focusing on performance analysis, spectrum allocation, and hybrid beamforming. Finally, I will discuss some pressing challenges for harnessing the benefits of THz communications in the next decades.

This talk starts by introducing some basic concepts in the field of graph signal processing including graph filtering (or graph convolution) and the conventional graph convolution theorem. The talk continues by extending this theorem to a general graph convolution theorem by introducing two notions: the node-varying graph filter and the dual graph. The node-varying graph filter broadens the applicability of the conventional graph filter by assigning different filter coefficients to different nodes, whereas the dual graph characterizes the structure of the graph frequency domain. Using these two notions, a general graph convolution theorem can be proposed which encompasses the conventional one and which can be seen as a translation of the convolution theorem for time-varying filters to the graph domain. Interestingly, using non-stationary graph data on the original (or primal) graph, we can use the proposed convolution theorem to learn the dual graph and thereby introduce an innovative data-driven dual graph estimation technique.

Geert Leus received the M.Sc. and Ph.D. degrees in Electrical Engineering from the KU Leuven, Belgium, in June 1996 and May 2000, respectively. Geert Leus is now a Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the broad area of signal processing, with a specific focus on wireless communications, array processing, sensor networks, and graph signal processing. Geert Leus received the 2021 EURASIP Individual Technical Achievement Award, a 2005 IEEE Signal Processing Society Best Paper Award, and a 2002 IEEE Signal Processing Society Young Author Best Paper Award. He is a Fellow of the IEEE and a Fellow of EURASIP. Geert Leus was a Member-at-Large of the Board of Governors of the IEEE Signal Processing Society, the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, the Chair of the EURASIP Technical Area Committee on Signal Processing for Multisensor Systems, a Member of the IEEE Sensor Array and Multichannel Technical Committee, a Member of the IEEE Big Data Special Interest Group, a Member of the EURASIP Signal Processing for Communications and Networking Special Area Team, the Editor in Chief of the EURASIP Journal on Advances in Signal Processing, and the Editor in Chief of EURASIP Signal Processing. He was also on the Editorial Boards of the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is a Member of the IEEE Signal Processing Theory and Methods Technical Committee and an Associate Editor of Foundations and Trends in Signal Processing.

This talk explores how to effectively transmit and decode the short messages that communicate control information in cellular communications and measurements and status updates from internet-of-things devices. While there are several techniques that can achieve (and in some cases out-perform) the random coding union upper bound on achievable frame error rate, the associated decoders typically have a high average complexity. This talk presents the concatenation of an expurgating linear function with a convolutional code as a technique that can outperform the RCU FER while requiring the average complexity of Viterbi decoding on a relatively small trellis. We will also explore communication of short messages with feedback, presenting new lower bounds on achievable rate for binary symmetric channel and a low complexity posterior-matching algorithm that achieves the lower bound.

Richard D. Wesel (Fellow, IEEE) received the B.S. and M.S. degrees in electrical engineering from the Massachusetts Institute of Technology in 1989, and the Ph.D. degree in electrical engineering from Stanford University in 1996. He is currently a Professor with the Electrical and Computer Engineering Department, UCLA, and an Associate Dean for Academic and Student Affairs for the Henry Samueli School of Engineering and Applied Science, UCLA. His research interests include communication theory with particular interest in low-density parity-check coding, short-blocklength communication with and without feedback, and coding for storage. He has received the National Science Foundation CAREER Award, the Okawa Foundation Award for research in information theory and telecommunications, and the Excellence in Teaching Award from the Samueli School of Engineering. He has served as an Associate Editor for Coding for the IEEE Transactions on Communications and the IEEE Transactions on Information Theory.

In this talk, I will introduce our recent work on effective beamforming design for massive multiuser MIMO and reconfigurable intelligent surface (RIS)-aided radar-communication coexistence (RCC) systems, respectively, via deep-unfolding and optimization techniques. In the first work, we propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum rate maximization problem for beamforming design in massive multiuser MIMO systems. Specifically, the iterative algorithms are unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. In the second work, we propose a double-RIS-assisted RCC system where two RISs are deployed for enhancing communication signals and suppressing mutual interference. We aim to jointly optimize the beamforming of RISs and radar to maximize communication performance while maintaining radar detection performance. The investigated problem is challenging, and thus we transform it into an equivalent but more tractable form by introducing auxiliary variables. Then, we propose a penalty dual decomposition (PDD)-based algorithm to solve the resultant problem. Simulation results verify the effectiveness of the proposed algorithms against the existing algorithms.

The rapid advancement of RF technology has opened up new possibilities for the development of innovative phased array architectures, revolutionizing communication, autonomous sensing, remote sensing, and weather observation capabilities. These cutting-edge phased arrays offer unparalleled performance, incorporating several key features that significantly enhance their functionality. One notable aspect of these new phased array architectures is their ability to achieve fast volumetric scanning updates in less than 20 seconds. Additionally, they boast wide-scanning capabilities, spanning an impressive range of 90 to 120 degrees.This presentation aims to shed light on the exciting potentials of these novel phased array architectures and their transformative impact on diverse fields, ranging from communication to remote sensing and weather observation. The remarkable features of fast volumetric scanning, wide-scanning range, and ultra-low cross-polarization isolation signify a new era of RF technology, offering unprecedented capabilities and opportunities for various applications.

Jorge L. Salazar-Cerreno received a B.S. In ECE from the University Antenor Orrego, Trujillo, Peru, M.S. degree in ECE from the University of Puerto Rico, Mayaguez (UPRM). In 2011, he received his Ph.D. degree in ECE from the University of Massachusetts, Amherst. His Ph.D. research focused on development of low-cost dual-polarized active phased array antennas (APAA) for the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). After graduation, Dr. Salazar-Cerreno was awarded a prestigious National Center for Atmospheric Research (NCAR) Advanced Study Program (ASP) postdoctoral fellowship. At NCAR, he worked at the Earth Observing Laboratory (EOL) division developing airborne technology for two-dimensional, electronically scanned, dual-pol phased array radars for atmospheric research. This is a critical tool for studying weather and related hazards, especially for retrieving dynamic and microphysical characteristics of clouds and precipitation over rugged terrain or the open ocean, where other radar systems can have major limitations. In July 2014, he joined the Advanced Radar Research Center (ARRC) at The University of Oklahoma as a research scientist, and became an associate professor at the School of Electrical and Computer Engineering in August 2021. His research interests include high-performance, broadband antennas for dual-polarized phased array radar applications; array antenna architecture for reconfigurable radar systems; APAA; Tx/Rx modules; radome EM modeling; RF and hardware development for characterizing and calibrating APAA and millimiter-waver antennas. In 2019, Dr. Salazar has been awarded a William H. Barkow Presidential Professorship. Presidential Professors inspire their students, mentor their undergraduate and graduate students in the process of research and creative scholarly activity within their discipline, and exemplify to their students and their colleagues the ideals of a scholar through their endeavors in teaching; research and creative scholarly activity; and professional and university service and public outreach. Dr. Salazar is a senior member of the IEEE and currently serves as a reviewer for IEEE Transactions on Antennas and Propagation (TAP), IET Microwaves, Antennas and Propagation (IET), the Journal of Atmospheric and Oceanic Technology (JTECH), IEEE Transactions on Geoscience and Remote Sensing (TGARS), John Wiley and Sons, and the Radio Science Journal.

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