Nirwan Ansari – författare

This book consists of an edited collection of chapters providing an overview of a wide range of telecommunications tasks being addressed with neural networks. These tasks range from the design and control of the underlying transport network to the filtering, interpretation and manipulation of the transported media. The chapters focus on specific applications, describe solutions and demonstrate the benefits that neural networks can provide. By doing this, the authors demonstrate that neural networks should be another tool used by telecommunications engineers. Neural networks offer the computational power of nonlinear techniques, while providing a natural path to efficient massively-parallel hardware implementations. In addition, the ability of neural networks to learn allows them to be used on problems where straight-forward heuristic, or rule-based, solutions do not exist. Together these capabilities mean that neural networks offer unique solutions to problems in telecommunications.This study is designed for engineers and managers in telecommunications, it aims to provide a single point of access to the work being done by leading researchers in this field, and to furnish the reader with an in-depth description of neural network applications.

The field of optimization is interdisciplinary in nature, and has made a significant impact on many areas of technology. As a result, optimization is an indispensable tool for many practitioners in various fields. Conventional optimization techniques are well established and widely published in many textbooks. However, there are new techniques, such as simulated annealing, mean field theory, and genetic algorithms, which have proven to be effective in solving global optimization problems. This text is intended as a technical description of the state-of-the-art developments in advanced optimization techniques, specifically simulated annealing, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementation, and practical applications. Most of the theories covered in this work and their applications are widely scattered in journals, technical reports, and conference proceedings of various fields, making it difficult for people new in the field to find them easily. The authors have brought together a comprehensive and organized treatment of these techniques, thus filling a gap in the scientific literature.Computational Intelligence for Optimization is suitable for first-year graduate courses in electrical and computer engineering, computer science, and operational research programs. It may also be used as a reference work for practising engineers, scientists, operational researchers and other specialists.

Green communications is a very hot topic. As mobile networks evolve in terms of higher rates/throughput, a consequent impact on operating costs is due to (aggregate) network energy consumption. As such, design on 4G networks and beyond have increasingly started to focus on `energy efficiency’ or so-called ‘green’ networks. Many techniques and solutions have been proposed to enhance the energy efficiency of mobile networks, yet no book has provided an in-depth analysis of the energy consumption issues in mobile networks nor has detailed theories, tools and solutions for solving the energy efficiency problems.This book presents the techniques and solutions for enhancing energy efficiency of future mobile networks, and consists of three major parts. The first part presents a general description of mobile network evolution in terms of both capacity and energy efficiency. The second part discusses the advanced techniques to green mobile networks. The third part discusses the solutions that enhance mobile network energy efficiency as well as provides future directions. Whilst the reader is expected to have basic knowledge of wireless communications, the authors present a brief introduction of the evolution of mobile networks, providing the knowledge base for understanding the content of the book. In addition, complicated network problems are illustrated using simple examples. This will help the reader understand the concept and intuition of various techniques and solutions. Incorporates the latest research results from both academia and industry, providing an up-to-date overview of existing technologies and solutions on making mobile networks greener Consists of three sections with a gradually increasing technical depth on green mobile networks, providing the reader with a systematic view of the research area, and helping those with different technical backgrounds to better understand the contentCovers existing enabling technologies for green mobile networking, including an innovative discussion of state-of-the-art solutions and algorithms

Neural Networks in Telecommunications consists of a carefully edited collection of chapters that provides an overview of a wide range of telecommunications tasks being addressed with neural networks. These tasks range from the design and control of the underlying transport network to the filtering, interpretation and manipulation of the transported media. The chapters focus on specific applications, describe specific solutions and demonstrate the benefits that neural networks can provide. By doing this, the authors demonstrate that neural networks should be another tool in the telecommunications engineer's toolbox. Neural networks offer the computational power of nonlinear techniques, while providing a natural path to efficient massively-parallel hardware implementations. In addition, the ability of neural networks to learn allows them to be used on problems where straightforward heuristic or rule-based solutions do not exist. Together these capabilities mean that neural networks offer unique solutions to problems in telecommunications. For engineers and managers in telecommunications, Neural Networks in Telecommunications provides a single point of access to the work being done by leading researchers in this field, and furnishes an in-depth description of neural network applications.

The field of optimization is interdisciplinary in nature, and has been making a significant impact on many disciplines. As a result, it is an indispensable tool for many practitioners in various fields. Conventional optimization techniques have been well established and widely published in many excellent textbooks. However, there are new techniques, such as neural networks, simulated anneal­ ing, stochastic machines, mean field theory, and genetic algorithms, which have been proven to be effective in solving global optimization problems. This book is intended to provide a technical description on the state-of-the-art development in advanced optimization techniques, specifically heuristic search, neural networks, simulated annealing, stochastic machines, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementa­ tion, and practical applications. The text is suitable for a first-year graduate course in electrical and computer engineering, computer science, and opera­ tional research programs. It may also be used as a reference for practicing engineers, scientists, operational researchers, and other specialists. This book is an outgrowth of a couple of special topic courses that we have been teaching for the past five years. In addition, it includes many results from our inter­ disciplinary research on the topic. The aforementioned advanced optimization techniques have received increasing attention over the last decade, but relatively few books have been produced.

This book focuses on various Passive optical networks (PONs)  types, including currently deployed Ethernet PON (EPON) and Gigabit PON (GPON) as well as next generation WDM PON and OFDM PON. Also this book examines the integrated optical and wireless access networks. Concentrating on two issues in these networks: media access control (MAC) and resource allocation. These two problems can greatly affect performances of PONs such as network resource utilization and QoS of end users. Finally this book will discuss various solutions to address the MAC and resource allocation issues in various PON networks.