Chandra R. Murthy – författare

This open access book is a practical guide to experimentally understand, analyze, and optimize the performance of 5G networks. It is organized as a series of simulation projects supplemented with the underlying theory, to help readers learn various aspects of 5G through hands-on simulations using NetSim. It therefore accomplishes two things simultaneously: It explains 5G networking and shows how to unlock the power of the NetSim simulation environment.Commercial deployments of 5G are well underway, covering at least a third of the world’s population. With such widespread use, engineers and researchers need tools for design and development of the 5G protocols, rapid prototyping, scalability testing, and research into complex network interactions. Simulators are often their first choice; they are cost effective, flexible, and easy to use. NetSim is an industry‑leading network simulation software used by over 400 organizations in more than 25 countries. Researchers and engineers worldwide rely on NetSim for R&D and for developing 5G‑related products. This book builds on that ecosystem by providing readers with structured, hands‑on projects that demonstrate exactly how NetSim can be used to model, test, and optimize real‑world 5G scenarios. By working through these simulations, readers gain both a deeper understanding of 5G technologies and practical experience using one of the most widely adopted tools in the field.

This volume provides a comprehensive overview of recent research advances in the upcoming field of sparse control and state estimation of linear dynamical systems. The contents offer a detailed introduction to the subject by combining classical control theory and compressed sensing. It covers conceptual foundations, including the formulation, theory, and algorithms, and outlines numerous remaining research challenges. Specifically, the book provides a detailed discussion on observability, controllability, and stabilizability under sparsity constraints. It also presents efficient, systematic, and rigorous approaches to estimating the sparse initial states and designing sparse control inputs. It also gives background materials from real analysis and probability theory and includes applications in network control, wireless communication, and image processing. It serves as a compendious source for graduate students and researchers in signal processing and control systems to acquire a thorough understanding of the underlying unified themes. The academic and industrial professionals working on the design and optimization of sparsity-constrained systems also benefit from the exposure to the array of recent works on linear dynamical systems and related mathematical machinery.