Xiaojun Yuan - Böcker

This Springerbreif  introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs.  The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity.Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where ‘scalable’ means that the computational and implementation complexities do not grow rapidly with the network size.This Springerbrief will be target researchers and professionals working in the Cloud Radio Access Network (C-Ran) field, as well as advanced-level students studying electrical engineering.

This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems. Provides an in depth look into turbo message passing algorithms for structured signal recoveryIncludes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passingShows applications in areas such as wireless communications and computer vision

This book provides an overview of the impact of the pandemic in China and the USA and presents a research agenda for use, access, and adoption of information and communication technologies (ICT) in the era of COVID-19.  The global COVID-19 pandemic changed lives overnight and exposed socially vulnerable populations to ever-challenging situations. One significant challenge was the use, access, and adoption of technological resources.  To understand how socially vulnerable populations managed the COVID-19 pandemic and adapt to the new normal, it is important for researchers and practitioners to identify the challenges and understand the perceptions of technologies.  Through various research studies, this edited volume addresses the impact of the global COVID-19 pandemic and the adoption of technologies (from artificial intelligence to telehealth and telecommunications) among some socially vulnerable populations (including children, older adults, COVID-19 patients, and general marginalized populations) in the world.  The information divide faced by socially vulnerable groups is studied as well as the dimension of vulnerabilities and the impacts of specific technologies.

This book provides an overview of the impact of the pandemic in China and the USA and presents a research agenda for use, access, and adoption of information and communication technologies (ICT) in the era of COVID-19.  The global COVID-19 pandemic changed lives overnight and exposed socially vulnerable populations to ever-challenging situations. One significant challenge was the use, access, and adoption of technological resources.  To understand how socially vulnerable populations managed the COVID-19 pandemic and adapt to the new normal, it is important for researchers and practitioners to identify the challenges and understand the perceptions of technologies.  Through various research studies, this edited volume addresses the impact of the global COVID-19 pandemic and the adoption of technologies (from artificial intelligence to telehealth and telecommunications) among some socially vulnerable populations (including children, older adults, COVID-19 patients, and general marginalized populations) in the world.  The information divide faced by socially vulnerable groups is studied as well as the dimension of vulnerabilities and the impacts of specific technologies.