Bio-Inspired Computation in Telecommunications

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.

Researchers in artificial intelligence, telecommunication engineers, computer scientists

• 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