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.
Xin She Yang is Senior Research Scientist in the Department of Mathematical and Scientific Computing at the National Physical Laboratory in the United Kingdom, Reader in Modeling and Optimization at Middlesex University, UK, and Adjunct Professor at Reykjavik University, Iceland. He is Editor-in-Chief of the International Journal of Mathematical Modelling and Numerical Optimization, a member of both the Society for Industrial and Applied Mathematics and the British Computer Society, a Fellow of The Royal Institution of Great Britain, and editor of seven additional books including Nature-Inspired Optimization Algorithms (Elsevier), Swarm Intelligence and Bio-Inspired Computation (Elsevier).
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.
Front Cover 1
Bio-Inspired Computation in Telecommunications 4
Copyright 5
Contents 6
Preface 14
List of Contributors 16
Chapter 1: Bio-Inspired Computation and Optimization: An Overview 20
1.1. Introduction 21
1.2. Telecommunications and optimization 21
1.3. Key challenges in optimization 23
1.3.1. Infinite Monkey Theorem and Heuristicity 23
1.3.2. Efficiency of an Algorithm 24
1.3.3. How to Choose Algorithms 24
1.3.4. Time Constraints 25
1.4. Bio-inspired optimization algorithms 26
1.4.1. SI-Based Algorithms 26
1.4.1.1. Ant and bee algorithms 26
1.4.1.2. Bat algorithm 27
1.4.1.3. Particle swarm optimization 28
1.4.1.4. Firefly algorithm 28
1.4.1.5. Cuckoo search 28
1.4.2. Non-SI-Based Algorithms 29
1.4.2.1. Simulated annealing 29
1.4.2.2. Genetic algorithms 30
1.4.2.3. Differential evolution 31
1.4.2.4. Harmony search 31
1.4.3. Other Algorithms 32
1.5. Artificial neural networks 32
1.5.1. Basic Idea 32
1.5.2. Neural Networks 33
1.5.3. Back Propagation Algorithm 34
1.6. Support vector machine 35
1.6.1. Linear SVM 35
1.6.2. Kernel Tricks and Nonlinear SVM 37
1.7. Conclusions 38
References 38
Chapter 2: Bio-Inspired Approaches in Telecommunications 42
2.1. Introduction 42
2.2. Design problems in telecommunications 44
2.3. Green communications 46
2.3.1. Energy Consumption in Wireless Communications 47
2.3.2. Metrics for Energy Efficiency 48
2.3.3. Radio Resource Management 50
2.3.4. Strategic Network Deployment 51
2.4. Orthogonal frequency division multiplexing 52
2.4.1. OFDM Systems 52
2.4.2. Three-Step Procedure for Timing and Frequency Synchronization 53
2.5. OFDMA model considering energy efficiency and quality-of-service 54
2.5.1. Mathematical Formulation 54
2.5.2. Results 56
2.6. Conclusions 57
References 57
Chapter 3: Firefly Algorithm in Telecommunications 62
3.1. Introduction 63
3.2. Firefly algorithm 65
3.2.1. Algorithm Complexity 67
3.2.2. Variants of Firefly Algorithm 67
3.3. Traffic Characterization 68
3.3.1. Network Management Based on Flow Analysis and Traffic Characterization 70
3.3.2. Firefly Harmonic Clustering Algorithm 71
3.3.3. Results 73
3.4. Applications in wireless cooperative networks 74
3.4.1. Related Work 77
3.4.2. System Model and Problem Statement 78
3.4.2.1. Energy and spectral efficiencies 80
3.4.2.2. Problem statement 81
3.4.3. Dinkelbach Method 81
3.4.4. Firefly Algorithm 83
3.4.5. Simulations and Numerical Results 84
3.5. Concluding remarks 89
3.5.1. FA in Traffic Characterization 89
3.5.2. FA in Cooperative Networks 89
References 89
Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation 92
4.1. Introduction 92
4.2. Intrusion detection systems 94
4.2.1. IDS Components 95
4.2.2. Research Areas and Challenges in Intrusion Detection 97
4.3. The method: evolutionary computation 98
4.4. Evolutionary computation applications on intrusion detection 99
4.4.1. Foundations 99
4.4.2. Data Collection 100
4.4.3. Detection Techniques and Response 102
4.4.3.1. Intrusion detection on conventional networks 102
4.4.3.2. Intrusion detection on wireless and resource-constrained networks 104
4.4.4. IDS Architecture 105
4.4.5. IDS Security 107
4.4.6. Testing and Evaluation 107
4.5. Conclusion and future directions 108
References 110
Chapter 5: VoIP Quality Prediction Model by Bio-Inspired Methods 114
5.1. Introduction 115
5.2. Speech quality measurement background 116
5.2.1. Subjective Methods 116
5.2.2. Intrusive Objective Methods 117
5.2.3. Nonintrusive Objective Methods 118
5.2.4. Bio-inspired Methods 119
5.3. Modeling methods 119
5.3.1. Methodology for Conversational Quality Prediction (PESQ/E-model) 119
5.3.1.1. Basic signal-to-noise ratio, R0 120
5.3.1.2. Delay impairment factor, Id 121
5.3.1.3. MOS-to-R conversion function and effective equipment impairment factor, Ie-eff 122
5.3.2. Nonlinear Surface Regression Model 123
5.3.3. Neural Network Model 124
5.3.4. REPTree Model 124
5.4. Experimental testbed 125
5.4.1. The Data Sets' Structure 127
5.4.1.1. Codec impairment data set 127
5.4.1.2. Human impairment data set 128
5.4.1.3. Mixed impairment data set 128
5.4.2. The Performance Measures 129
5.5. Results and discussion 129
5.5.1. Correlation Comparison 129
5.5.2. Residual Analysis 130
5.6. Conclusions 132
References 134
Chapter 6: On the Impact of the Differential Evolution Parameters in the Solution of the Survivable Virtual Topology-Mapp... 136
6.1. Introduction 136
6.2. Problem Formulation 139
6.3. DE Algorithm 140
6.3.1. Fitness of an Individual 142
6.3.2. Pseudocode of the DE Algorithm 142
6.3.3. Enhanced DE-VTM Algorithm 143
6.4. Illustrative Example 143
6.5. Results and Discussion 147
6.6. Conclusions 158
References 158
Chapter 7: Radio Resource Management by Evolutionary Algorithms for 4G LTE-Advanced Networks 160
7.1. Introduction to radio resource management 161
7.1.1. Frame Structure 162
7.1.2. DL and Uplink 162
7.2. LTE-A technologies 164
7.2.1. Carrier Aggregation 164
7.2.2. Relay Nodes 164
7.2.3. Femtocell 165
7.2.4. Coordinated Multipoint Transmission 165
7.3. Self-organization using evolutionary algorithms 166
7.3.1. SON Physical Layer 166
7.3.2. SON MAC Layer 167
7.3.3. SON Network Layer 167
7.3.4. LTE-A Open Research Issues and Challenges 168
7.4. EAs in LTE-A 169
7.4.1. Network Planning 170
7.4.2. Network Scheduling 171
7.4.3. Energy Efficiency 172
7.4.4. Load Balancing 173
7.4.5. Resource Allocation 174
7.4.5.1. Genetic algorithm 174
7.4.5.2. Game theory 175
7.5. Conclusion 180
References 181
Chapter 8: Robust Transmission for Heterogeneous Networks with Cognitive Small Cells 184
8.1. Introduction 184
8.2. Spectrum sensing for cognitive radio 186
8.3. Underlay spectrum sharing 187
8.3.1. Underlay Spectrum Sharing for Heterogeneous Networks with MIMO Channels 188
8.3.2. Underlay Spectrum Sharing for Heterogeneous Networks with Doubly Selective Fading SISO Channels 188
8.4. System Model 189
8.4.1. System Model with MIMO Channel 189
8.4.2. System Model with Doubly Fading Selective SISO Channel 189
8.5. Problem Formulation 190
8.6. Sparsity-enhanced mismatch model (SEMM) 192
8.7. Sparsity-enhanced mismatch model-reverse DPSS (SEMMR) 194
8.8. Precoder design using the SEMM and SEMMR 196
8.8.1. SEMM Precoder Design 196
8.8.2. Second-stage SEMMR Precoder and Decoder Design 197
8.9. Simulation results 199
8.9.1. SEMM Precoder 199
8.9.2. SEMMR Transceiver 200
8.10. Conclusion 201
References 202
Chapter 9: Ecologically Inspired Resource Distribution Techniques for Sustainable Communication Networks 204
9.1. Introduction 204
9.2. Consumer-Resource Dynamics 205
9.3. Resource Competition in the NGN 207
9.4. Conditions for Stability and Coexistence 211
9.5. Application for LTE Load Balancing 214
9.6. Validation and Results 216
9.7. Conclusions 220
References 220
Chapter 10: Multiobjective Optimization in Optical Networks 224
10.1. Introduction 225
10.1.1. Common Optical Network Problems in a Multiobjective Context 225
10.2. Multiobjective Optimization 227
10.2.1. Multiobjective Optimization Formulation 227
10.2.2. Multiobjective Performance Metrics 228
10.2.3. Experimental Methodology 229
10.2.4. Algorithms to Solve MOPs 231
10.2.4.1. Types of optimization problems and WDM networks 231
10.2.4.2. Evolutionary algorithms 232
10.2.4.3. Ant colony optimization 233
10.3. RWA Problem 234
10.3.1. Traditional RWA 234
10.3.2. Multiobjective RWA Formulation 235
10.3.3. ACO for RWA 235
10.3.4. MOACO for RWA 236
10.3.5. Classical Heuristics 239
10.3.6. Simulations 240
10.3.7. Experimental Results 241
10.4. WCA Problem 243
10.4.1. Related Work 244
10.4.2. Classical Problem Formulation 245
10.4.3. Multiobjective Formulation 246
10.4.4. Traffic Models and Simulation Algorithm 246
10.4.5. EA for WCA 247
10.4.6. Experimental Results 248
10.4.6.1. Numerical results 249
10.5. p-Cycle Protection 251
10.5.1. Problem Formulation 254
10.5.2. Generating Candidate Cycles 255
10.5.3. Multiobjective Evolutionary Algorithms 256
10.5.4. Experimental Results 257
10.6. Conclusions 258
References 259
Chapter 11: Cell-Coverage-Area Optimization Based on Particle Swarm Optimization (PSO) for Green Macro Long-Term Evolutio... 264
11.1. Introduction 264
11.2. Related works 265
11.3. Mechanism of proposed cell-switching scheme 267
11.4. System model and problem formulation 269
11.5. PSO algorithm 271
11.6. Simulation results and discussion 273
11.6.1. Simulation Setup 273
11.6.2. Simulation Flow Chart 273
11.6.3. Results and Discussion 274
11.6.4. Energy and OPEX Savings 278
11.7. Conclusion 279
References 280
Chapter 12: Bio-Inspired Computation for Solving the Optimal Coverage Problem in Wireless Sensor Networks 282
12.1. Introduction 283
12.2. Optimal Coverage Problem in WSN 285
12.2.1. Problem Formulation 285
12.2.2. Related Work 287
12.2.3. Bio-Inspired PSO 288
12.3. BPSO for OCP 288
12.3.1. Solution Representation and Fitness Function 288
12.3.2. Initialization 289
12.3.3. BPSO Operations 290
12.3.4. Maximizing the Disjoint Sets 291
12.4. Experiments and Comparisons 291
12.4.1. Algorithm Configurations 291
12.4.2. Comparisons with State-of-the-Art Approaches 292
12.4.3. Comparisons with the GA Approach 293
12.4.4. Extensive Experiments on Different Scale Networks 295
12.4.5. Results on Maximizing the Disjoint Sets 297
12.5. Conclusion 301
References 302
Chapter 13: Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless Mesh Networks 306
13.1. Introduction 307
13.2. Problem Formulation 309
13.2.1. System Model 309
13.2.2. Channel Assignment Problem 311
13.2.3. Related Channel Assignment Algorithms 313
13.3. Clonal-Selection-Based Algorithms for the Channel Assignment Problem 314
13.3.1. Phase One 315
13.3.1.1. Initialization 315
13.3.1.2. Affinity evaluation 316
13.3.1.3. Clonal selection and expansion 318
13.3.1.3.1. CLONALG 318
13.3.1.3.2. BCA 319
13.3.1.3.3. CLIGA 320
13.3.1.4. Local search 321
13.3.2. Phase Two 321
13.3.3. Variants of the Channel Assignment Algorithm 322
13.4. Performance Evaluation 323
13.4.1. Comparison with Other Channel Assignment Algorithms 325
13.4.2. Convergence of IA 327
13.4.3. Impact of Parameter Setting 328
13.4.4. Impact of Local Search 331
13.4.5. Variants of Channel Assignment Algorithm 332
13.5. Concluding remarks 337
References 339
Index 342
Bio-Inspired Approaches in Telecommunications
Su Fong Chien1 sf.chien@mimos.my; C.C. Zarakovitis2; Tiew On Ting3; Xin-She Yang4 1 Strategic Advanced Research (StAR) Mathematical Modeling Lab, MIMOS Berhad, Kuala Lumpur, Malaysia
2 School of Computing and Communications, Lancaster University, Lancaster, UK
3 Department of Electrical and Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China
4 School of Science and Technology, Middlesex University, London, UK
Abstract
Bio-inspired algorithms are modern optimization tools that are capable of solving complex design problems in many applications. Such algorithms aim to speed up the optimization process so as to tackle tougher optimization problems. Some of these algorithms, such as particle swarm optimization and cuckoo search, have been found to be much more feasible and practical in obtaining the optimal solution, compared to conventional mathematical methods. In this chapter, we will review design problems and their solution methods concerning resource and power allocations in orthogonal frequency division multiple access systems.
Keywords
Bio-inspired algorithm
Energy efficiency
Spectral efficiency
Subchannel allocation
OFDMA
Convex optimization
Chapter Contents
2.1 Introduction 23
2.2 Design Problems in Telecommunications 25
2.3 Green Communications 27
2.3.1 Energy Consumption in Wireless Communications 28
2.3.2 Metrics for Energy Efficiency 29
2.3.3 Radio Resource Management 31
2.3.4 Strategic Network Deployment 32
2.4 Orthogonal Frequency Division Multiplexing 33
2.4.1 OFDM Systems 33
2.4.2 Three-Step Procedure for Timing and Frequency Synchronization 34
2.5 OFDMA Model Considering Energy Efficiency and Quality-of-Service 35
2.5.1 Mathematical Formulation 35
2.5.2 Results 37
2.6 Conclusions 38
References 38
2.1 Introduction
Bio-inspired algorithms have become popular optimization tools to tackle complex design problems. With the steady advancement of computing facilities, both scientists and engineers have started to utilize bio-inspired algorithms due to their advantages over conventional methods (Yang and Koziel, 2011; Yang, 2014). A major characteristic of bio-inspired algorithms is that they are flexible and straightforward to implement, yet efficient to solve tough problems in applications such as engineering and telecommunications. In fact, bio-inspired computation in telecommunications has a rather rich history.
Probably the first application of a multiobjective bio-inspired algorithm was attempted by J.D. Schaffer in the mid-1980s (Schaffer, 1984). A considerable extension in this area is now known as multiobjective evolutionary algorithm. The activities have been reflected by the ever-increasing number of technical papers published in conferences, journals, and books. An important advantage of bio-inspired algorithms on the solutions of multiobjective optimization problems is that they are able to produce feasible solutions in which Pareto optimal sets are attainable in a single run of the algorithms (Coello, 1999). Compared with the conventional methods, bio-inspired methods may take longer to run but they can indeed produce satisfactory solutions (or even global optimal solutions). In addition, bio-inspired algorithms are less sensitive to the shape or continuity of the Pareto front, avoiding some disadvantages of conventional mathematical programming (Coello et al., 2002). The importance of bio-inspired algorithms in communications and networking can be seen from the extensive literature survey done by Kampstra et al. (2006), where more than 350 references were listed on the applications of bio-inspired techniques for solving telecommunication design problems.
Design problems in telecommunications tend to be complex and large-scale, and thus computationally demanding. Such issues become even more challenging due to the increasing demands of bandwidth as well as disruption-free services in wireless communications, which is in addition to quality-of-service (QoS) constraints for subscribers. The complexity of such problems means that conventional methods are not able to meet these challenges. In recent years, bio-inspired methods have become powerful alternative techniques to deal with design problems in telecommunications. In fact, both types of techniques complement each other in terms of simplicity, efficiency, and transparency in communication for the end users. However, architectural redesign efforts are very time-consuming and thus can be very costly, and consequently, there is an ever-increasing demand for efficient techniques that can support proper design requirements.
In essence, common problems that need to be addressed in telecommunications areas include node location problems, network topology design problems, routing and path restoration problems, efficient admission control mechanisms, channel and/or wavelength assignments and resource allocation problems, and so on. Due to the complicated nature of communications infrastructures, such design problems become even more complex. Hence, all these necessitate a multiobjective approach subject to noise, the dynamic behavior of parameters, and large solution spaces. As a result, conventional methods are not capable of solving such problems effectively (Routen, 1994).
The main aim of this chapter is to review the types of design problems in telecommunications and their solution strategies. Thus, Section 2.2 outlines the seven types of design problems, and Section 2.3 discusses green communications. Section 2.4 briefly introduces orthogonal frequency division multiplexing (OFDM), and Section 2.5 presents a case study of orthogonal frequency division multiple access (OFDMA) with the consideration of energy efficiency (EE). Finally, Section 2.6 draws some conclusions.
2.2 Design problems in telecommunications
There is a wide range of design problems in telecommunications. A survey by P. Kampstra et al. has classified such design problems into seven major categories (Kampstra et al., 2006). The first category regards node location problems. One of the problems concerns the placement of concentrators in a local access network, where the genetic algorithm (GA) was applied in this case (Calégari et al., 1997). As discussed by Calégari et al. (1997), the key issue for a radio network is the proper positioning of locations of antennas and receivers, whereas the problem of the proper selection of base stations (BSs) was studied by Krzanowski and Raper (1999). In fact, most problems were attempted by GAs with satisfactory results.
The second category refers to topology design in computer networking: GAs where the key tool compared to others. Topologies for computer networks focused on reliability problems (Kumar et al., 1995). In their study, they used a variant of the GA, together with some problem-specific repair and crossover functions. Survivable military communication networks were also investigated, considering the damaging impact from the network with some satellite links (Sobotka, 1992). Nevertheless, network reliability is not the only important factor; backbone topologies must also take into account the economic costs (Deeter and Smith, 1998; Konak and Smith, 2004). To meet the ever-increasing demand for bandwidth and speed, different types of telecommunication technologies such as asynchronous transfer mode (ATM) networks had become an alternative for future networks. To a certain extent, ATM network topology designs have been rigorously studied (Tang et al., 1998; Thompson and Bilbro, 2000).
Moreover, as backbone technologies evolve, the subscriptions of video-on-demand services become prevalent. A proper network design with storage nodes for videos has become one of the important topics (Tanaka and Berlage, 1996). Another application area that is worth studying is the way of assigning terminals to concentrators. GAs were used to assign terminals to concentrators, powered by permutation encoding. It was found that GAs can outperform the greedy algorithm (Abuali et al., 1994). In the era of multimedia traffic, technology that has to support the huge bandwidth of at least several of tens of terabit per second becomes a must...
| Erscheint lt. Verlag | 11.2.2015 |
|---|---|
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Algebra | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 0-12-801743-0 / 0128017430 |
| ISBN-13 | 978-0-12-801743-2 / 9780128017432 |
| Haben Sie eine Frage zum Produkt? |
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