Applied Optimization Methods for Wireless Networks

Applied Optimization Methods for Wireless Networks

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Description

Written in a unique style, this book is a valuable resource for faculty, graduate students, and researchers in the communications and networking area whose work interfaces with optimization. It teaches you how various optimization methods can be applied to solve complex problems in wireless networks. Each chapter reviews a specific optimization method and then demonstrates how to apply the theory in practice through a detailed case study taken from state-of-the-art research. You will learn various tips and step-by-step instructions for developing optimization models, reformulations, and transformations, particularly in the context of cross-layer optimization problems in wireless networks involving flow routing (network layer), scheduling (link layer), and power control (physical layer). Throughout, a combination of techniques from both operations research and computer science disciplines provides a holistic treatment of optimization methods and their applications. Each chapter includes homework exercises, with PowerPoint slides and a solutions manual for instructors available online.show more

Product details

  • Electronic book text
  • CAMBRIDGE UNIVERSITY PRESS
  • Cambridge University Press (Virtual Publishing)
  • Cambridge, United Kingdom
  • English
  • 97 b/w illus. 64 tables 135 exercises
  • 1139088467
  • 9781139088466

Table of contents

1. Introduction; Part I. Methods for Optimal Solutions: 2. Linear programming and applications; 3. Convex programming and applications; 4. Design of polynomial-time exact algorithm; Part II. Methods for Near-Optimal and Approximation Solutions: 5. Branch-and-bound framework and application; 6. Reformulation-linearization technique and applications; 7. Linear approximation; 8. Approximation algorithm and its applications - part 1; 9. Approximation algorithm and its applications - part 2; Part III. Methods for Efficient Heuristic Solutions: 10. An efficient technique for mixed-integer optimization; 11. Metaheuristic methods; Part IV. Other Topics: 12. Asymptotic capacity analysis.show more