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Bio-Inspired Computation in Telecommunications

Book Description

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.

Table of Contents

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