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Look Inside Energy and Spectrum Efficient Wireless Network Design

Energy and Spectrum Efficient Wireless Network Design

$63.99 (C)

Authors:
Guowang Miao, Royal Institute of Technology, Stockholm
Guocong Song, ShareThis, California

Date Published: December 2014
availability: Available
format: Hardback
isbn: 9781107039889

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About the Authors

Covering the fundamental principles and state-of-the-art cross-layer techniques, this practical guide provides the tools needed to design MIMO- and OFDM-based wireless networks that are both energy- and spectrum-efficient. Technologies are introduced in parallel for both centralized and distributed wireless networks to give you a clear understanding of the similarities and differences between their energy- and spectrum-efficient designs, which is essential for achieving the highest network energy saving without losing performance. Cutting-edge green cellular network design technologies, enabling you to master resource management for next-generation wireless networks based on MIMO and OFDM, and detailed real-world implementation examples are provided to guide your engineering design in both theory and practice. Whether you are a graduate student, a researcher or a practitioner in industry, this is an invaluable guide.
- Provides an in-depth understanding of the similarities and difference between centralized and distributed cross-layer design, essential for achieving optimal network energy efficiency
- Includes detailed, real-world implementation examples to guide engineering designs in practice
- Covers state-of-the-art green cellular network design technologies, enabling readers to master resource management for next-generation wireless networks based on MIMO and OFDM
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Product details
- Date Published: December 2014
- format: Hardback
- isbn: 9781107039889
- length: 400 pages
- dimensions: 254 x 179 x 21 mm
- weight: 0.93kg
- contains: 122 b/w illus. 20 tables
- availability: Available
Table of Contents
1. Introduction
Part I. Basic Concepts:
2. Wireless channel properties
3. Spectral and energy efficiencies of wireless networks
4. Centralized resource management in wireless networks
5. Distributed resource management in wireless networks
Part II. Centralized Cross-Layer Optimization:
6. Overview
7. Utility-based optimization framework for OFDMA
8. Algorithm development for utility-based optimization
9. Joint channel and queue-aware multicarrier scheduling using delay-based utility functions
10. Utility-based generalized QoS scheduling for heterogeneous traffic
11. Asymptotic performance analysis for channel-aware scheduling
Part III. Distributed Cross-Layer Optimization:
12. Overview
13. Opportunistic random access: single-cell cellular networks
14. Opportunistic random access: any network topology
15. Optimal channel-aware distributed MAC
16. Opportunistic random access with intelligent interference avoidance
17. Distributed power control
Part IV. Cross-Layer Optimization for Energy-Efficient Networks:
18. Overview
19. Energy-efficient transmission
20. Centralized energy-efficient wireless resource management
21. Distributed energy-efficient wireless resource management
22. Energy-efficient cellular network design
23. Implementation in practice.
Look Inside
Authors
Guowang Miao, Royal Institute of Technology, Stockholm
Guowang Miao is an Assistant Professor in the Department of Communications Systems at KTH Royal Institute of Technology, Sweden. After receiving his PhD in electrical and computer engineering from Georgia Institute of Technology, USA, in 2009, he spent two years working in industry as a Senior Standard Engineer at Samsung Telecom America. His current research interests are in the design and optimization of wireless communications and networking.
Guocong Song, ShareThis, California
Guocong Song is currently Principal Research Engineer at ShareThis, Palo Alto, California. He has been working in wireless communications and networks for a decade, since receiving his PhD in electrical and computer engineering from Georgia Institute of Technology. He received the 2010 IEEE Stephen O. Rice Prize for the best paper in the field of communications theory, and he is recently active in the area of data science and machine learning.