Optimization Methods in Mobile Communication Systems : A Machine-generated Literature Overview

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. This machine-generated volume, with chapter introductions by the human expert, of summaries of the existing studies furthers our understanding of the optimization methods in mobile communication systems. The book provides a machine generated comprehensive yet classified review of the optimization methods, techniques and approaches associated with different mobile communications and systems including wavelet-based CR-OFDM systems, MIMO slot antenna with low mutual coupling for 5G networks, etc. It also covers mainly the techniques for performance analysis of MIMO systems such as the hybrid filtering technique for MIMO-OFDM systems and artificial intelligence solutions beyond 5G radio access networks.

Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples.

It is important to us as a publisher to make advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

Milind Pande is currently the Pro-Vice Chancellor at MIT World Peace University, Pune, India. He is a dedicated professional with 25 years of academic experience and has traveled extensively for technology transfers, international collaborations, and faculty/student exchange programs. Dr. Pande is one of the rare recipients of a Post-Doctoral Research Fellowship by the Ministry of HRD, Govt. of India, on Telecom Technology Transfer. He has been working towards realizing the dream of former President of India, Dr. A. P. J. Abdul Kalam, on the Project “Providing Urban Opportunities in Rural Areas” (PURA). He has co-authored a textbook on Mobile Communications with a focus on the Indian Telecom ecosystem. After graduating in Engineering, Dr. Pande acquired three master’s degrees: ME (Electronics), MBA (Marketing), and MPM (Personnel Management). He holds two PhDs, one in International Trade and the other in Wireless Technology. Dr. Pande’s research areas include technology transfers, m-education, m-agriculture, m-health, smart sensing networks, and ICT innovations.
Anand J. Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore, an MS in Artificial Intelligence from the University of Regina, Canada, a Bachelor of Engineering from Shivaji University, India, and a Diploma from MSBTE, Mumbai. He worked as a Postdoctoral Research Fellow at the Odette School of Business, University of Windsor, Canada. Dr. Kulkarni has over six years of experience with Symbiosis International University, Pune, India. Currently, he is a Professor and Associate Director at the Institute of AI at MITWPU. His research interests include optimization algorithms, multi-agent systems, complex systems, swarm optimization, and self-organizing systems. Anand pioneered socio-inspired optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, and Socio-Evolution and Learning Optimization Algorithm. He is the founder and chairman of the Optimization and Agent Technology Research Lab and has over 70 research papers in journals and conferences, 4 authored books, and 8 edited books to his credit.
Apoorva S Shastri holds a Ph.D. in Optimization Algorithms and Applications from Symbiosis International (Deemed to be) University, M. Tech in VLSI Design, and  B.Tech. in Electronics & Product Design Technology from R.T.M.N.U, Nagpur. She has also earned a Diploma from the Govt. Polytechnic, Nagpur. She worked as a guest faculty at the Centre for Development of Advanced Computing (C-DAC), Pune. Currently, she is a Research Assistant Professor at the Institute of Artificial Intelligence at the MITWPU, Pune, India. Her research interests include optimization algorithms, VLSI design, multi-objective optimization, continuous, discrete, and combinatorial optimization, complex systems, manufacturing, and self-organizing systems. Apoorva developed socio-inspired optimization methodologies such as multi-cohort intelligence algorithm and expectation algorithm. Dr. Shastri has published several research papers in peer-reviewed journals, chapters, and conferences.