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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
In the recent past, many components of modern infrastructure such as transportation systems, power systems, climate and environment monitoring systems, education systems and even government are being increasingly interconnected through information networks. Central to the functioning of current-day information networks are strategies that facilitate distributed network information processing objectives. In this monograph, the authors address the overarching challenge of designing efficient information processing strategies from a fundamental network information theory viewpoint. The authors address several network communication problems which can be considered as building blocks of networks. They consider these problems from both the data transmission and the data storage perspectives. They devise structured coding schemes for the finite alphabet cases of these problems and for each problem provide at least one example where they prove that the structured coding scheme is optimal, whereas the unstructured coding scheme is strictly suboptimal. Toward studying the information-theoretic performance limits in each of these communication scenarios, they consider two key concepts: common information and code structure. They uncover a new fundamental connection between them, and develop the key elements of a unified coding framework.This monograph is aimed at students, researchers and practitioners in information theory and communications. It provides an in-depth discussion of the theory and techniques resulting in a framework that the reader can apply to further their own work.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
In the recent past, many components of modern infrastructure such as transportation systems, power systems, climate and environment monitoring systems, education systems and even government are being increasingly interconnected through information networks. Central to the functioning of current-day information networks are strategies that facilitate distributed network information processing objectives. In this monograph, the authors address the overarching challenge of designing efficient information processing strategies from a fundamental network information theory viewpoint. The authors address several network communication problems which can be considered as building blocks of networks. They consider these problems from both the data transmission and the data storage perspectives. They devise structured coding schemes for the finite alphabet cases of these problems and for each problem provide at least one example where they prove that the structured coding scheme is optimal, whereas the unstructured coding scheme is strictly suboptimal. Toward studying the information-theoretic performance limits in each of these communication scenarios, they consider two key concepts: common information and code structure. They uncover a new fundamental connection between them, and develop the key elements of a unified coding framework.This monograph is aimed at students, researchers and practitioners in information theory and communications. It provides an in-depth discussion of the theory and techniques resulting in a framework that the reader can apply to further their own work.