Purchase textbook

Bio-Inspired Computation in Telecommunications,

Edition 1

Editors: By Xin-She Yang, Su Fong Chien and T.O. Ting

Publication Date: 06 Feb 2015

0 Reviews

Legal Considerations

Unknown accessibility

Note

This product relies on 3rd party tooling which may impact the accessibility features visible in inspection copies. All accessibility features mentioned would be present in the purchased version of the title.

Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

About the author

By Xin-She Yang, School of Science and Technology, Middlesex University, UK; Su Fong Chien, Associate Professor of Engineering & Technology, Multimedia University, Selangor, Malaysia and T.O. Ting, Lecturer, Department of Electrical and Electronic Engineering, Xian Jiaotong-Liverpool University, Jiangsu, China

Preface
List of Contributors
Chapter 1: Bio-Inspired Computation and Optimization: An Overview
- Abstract
- 1.1 Introduction
- 1.2 Telecommunications and optimization
- 1.3 Key challenges in optimization
- 1.4 Bio-inspired optimization algorithms
- 1.5 Artificial neural networks
- 1.6 Support vector machine
- 1.7 Conclusions
Chapter 2: Bio-Inspired Approaches in Telecommunications
- Abstract
- 2.1 Introduction
- 2.2 Design problems in telecommunications
- 2.3 Green communications
- 2.4 Orthogonal frequency division multiplexing
- 2.5 OFDMA model considering energy efficiency and quality-of-service
- 2.6 Conclusions
Chapter 3: Firefly Algorithm in Telecommunications
- Abstract
- 3.1 Introduction
- 3.2 Firefly algorithm
- 3.3 Traffic characterization
- 3.4 Applications in wireless cooperative networks
- 3.5 Concluding remarks
Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation
- Abstract
- Acknowledgments
- 4.1 Introduction
- 4.2 Intrusion detection systems
- 4.3 The method: evolutionary computation
- 4.4 Evolutionary computation applications on intrusion detection
- 4.5 Conclusion and future directions
Chapter 5: VoIP Quality Prediction Model by Bio-Inspired Methods
- Abstract
- 5.1 Introduction
- 5.2 Speech quality measurement background
- 5.3 Modeling methods
- 5.4 Experimental testbed
- 5.5 Results and discussion
- 5.6 Conclusions
Chapter 6: On the Impact of the Differential Evolution Parameters in the Solution of the Survivable Virtual Topology-Mapping Problem in IP-Over-WDM Networks
- Abstract
- 6.1 Introduction
- 6.2 Problem formulation
- 6.3 DE algorithm
- 6.4 Illustrative example
- 6.5 Results and discussion
- 6.6 Conclusions
Chapter 7: Radio Resource Management by Evolutionary Algorithms for 4G LTE-Advanced Networks
- Abstract
- 7.1 Introduction to radio resource management
- 7.2 LTE-A technologies
- 7.3 Self-organization using evolutionary algorithms
- 7.4 EAs in LTE-A
- 7.5 Conclusion
Chapter 8: Robust Transmission for Heterogeneous Networks with Cognitive Small Cells
- Abstract
- 8.1 Introduction
- 8.2 Spectrum sensing for cognitive radio
- 8.3 Underlay spectrum sharing
- 8.4 System Model
- 8.5 Problem formulation
- 8.6 Sparsity-enhanced mismatch model (SEMM)
- 8.7 Sparsity-enhanced mismatch model-reverse DPSS (SEMMR)
- 8.8 Precoder design using the SEMM and SEMMR
- 8.9 Simulation results
- 8.10 Conclusion
Chapter 9: Ecologically Inspired Resource Distribution Techniques for Sustainable Communication Networks
- Abstract
- 9.1 Introduction
- 9.2 Consumer-resource dynamics
- 9.3 Resource competition in the NGN
- 9.4 Conditions for stability and coexistence
- 9.5 Application for LTE load balancing
- 9.6 Validation and results
- 9.7 Conclusions
Chapter 10: Multiobjective Optimization in Optical Networks
- Abstract
- 10.1 Introduction
- 10.2 Multiobjective optimization
- 10.3 RWA Problem
- 10.4 WCA Problem
- 10.5 p-Cycle protection
- 10.6 Conclusions
Chapter 11: Cell-Coverage-Area Optimization Based on Particle Swarm Optimization (PSO) for Green Macro Long-Term Evolution (LTE) Cellular Networks
- Abstract
- Acknowledgment
- 11.1 Introduction
- 11.2 Related works
- 11.3 Mechanism of proposed cell-switching scheme
- 11.4 System model and problem formulation
- 11.5 PSO algorithm
- 11.6 Simulation results and discussion
- 11.7 Conclusion
Chapter 12: Bio-Inspired Computation for Solving the Optimal Coverage Problem in Wireless Sensor Networks: A Binary Particle Swarm Optimization Approach
- Abstract
- Acknowledgments
- 12.1 Introduction
- 12.2 Optimal coverage problem in WSN
- 12.3 BPSO for OCP
- 12.4 Experiments and comparisons
- 12.5 Conclusion
Chapter 13: Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless Mesh Networks
- Abstract
- 13.1 Introduction
- 13.2 Problem formulation
- 13.3 Clonal-Selection-Based algorithms for the channel assignment problem
- 13.4 Performance evaluation
- 13.5 Concluding remarks
Index

Book Reviews

"...reading this book will broaden your horizons with regard to how one could solve optimization problems by applying bio-inspired algorithms, with particular emphasis on telecommunications networks...It could be used for courses related to telecommunications, as well as for courses related to advanced algorithmics."—Computing Reviews

ISBN: 9780128015384

Page Count: 348

Retail Price :

Yang, Swarm Intelligence and Bio-Inspired Computation, Elsevier, 9780124051638, May 2013 450 pgs., $125.00
Yang, Nature-Inspired Optimization Algorithms, Elsevier, 9780124167438, Mar 2013, 300 pgs, $74.96

Researchers in artificial intelligence, telecommunication engineers, computer scientists