Yang / Chien / Ting Bio-Inspired Computation in Telecommunications

1. Auflage 2015
ISBN: 978-0-12-801743-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 348 Seiten

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 obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).

Autoren/Hrsg.

Yang, Xin-She

Chien, Su Fong

Ting, T. O.

Inhaltsverzeichnis

1;Front Cover;1
2;Bio-Inspired Computation in Telecommunications;4
3;Copyright ;5
4;Contents ;6
5;Preface ;14
6;List of Contributors ;16
7;Chapter 1: Bio-Inspired Computation and Optimization: An Overview;20
7.1;1.1. Introduction;21
7.2;1.2. Telecommunications and optimization;21
7.3;1.3. Key challenges in optimization;23
7.3.1;1.3.1. Infinite Monkey Theorem and Heuristicity;23
7.3.2;1.3.2. Efficiency of an Algorithm;24
7.3.3;1.3.3. How to Choose Algorithms;24
7.3.4;1.3.4. Time Constraints;25
7.4;1.4. Bio-inspired optimization algorithms;26
7.4.1;1.4.1. SI-Based Algorithms;26
7.4.1.1;1.4.1.1. Ant and bee algorithms;26
7.4.1.2;1.4.1.2. Bat algorithm;27
7.4.1.3;1.4.1.3. Particle swarm optimization;28
7.4.1.4;1.4.1.4. Firefly algorithm;28
7.4.1.5;1.4.1.5. Cuckoo search;28
7.4.2;1.4.2. Non-SI-Based Algorithms;29
7.4.2.1;1.4.2.1. Simulated annealing;29
7.4.2.2;1.4.2.2. Genetic algorithms;30
7.4.2.3;1.4.2.3. Differential evolution;31
7.4.2.4;1.4.2.4. Harmony search;31
7.4.3;1.4.3. Other Algorithms;32
7.5;1.5. Artificial neural networks;32
7.5.1;1.5.1. Basic Idea;32
7.5.2;1.5.2. Neural Networks;33
7.5.3;1.5.3. Back Propagation Algorithm;34
7.6;1.6. Support vector machine;35
7.6.1;1.6.1. Linear SVM;35
7.6.2;1.6.2. Kernel Tricks and Nonlinear SVM;37
7.7;1.7. Conclusions;38
7.8;References;38
8;Chapter 2: Bio-Inspired Approaches in Telecommunications;42
8.1;2.1. Introduction;42
8.2;2.2. Design problems in telecommunications;44
8.3;2.3. Green communications;46
8.3.1;2.3.1. Energy Consumption in Wireless Communications;47
8.3.2;2.3.2. Metrics for Energy Efficiency;48
8.3.3;2.3.3. Radio Resource Management;50
8.3.4;2.3.4. Strategic Network Deployment;51
8.4;2.4. Orthogonal frequency division multiplexing;52
8.4.1;2.4.1. OFDM Systems;52
8.4.2;2.4.2. Three-Step Procedure for Timing and Frequency Synchronization;53
8.5;2.5. OFDMA model considering energy efficiency and quality-of-service;54
8.5.1;2.5.1. Mathematical Formulation;54
8.5.2;2.5.2. Results;56
8.6;2.6. Conclusions;57
8.7;References;57
9;Chapter 3: Firefly Algorithm in Telecommunications;62
9.1;3.1. Introduction;63
9.2;3.2. Firefly algorithm;65
9.2.1;3.2.1. Algorithm Complexity;67
9.2.2;3.2.2. Variants of Firefly Algorithm;67
9.3;3.3. Traffic Characterization;68
9.3.1;3.3.1. Network Management Based on Flow Analysis and Traffic Characterization;70
9.3.2;3.3.2. Firefly Harmonic Clustering Algorithm;71
9.3.3;3.3.3. Results;73
9.4;3.4. Applications in wireless cooperative networks;74
9.4.1;3.4.1. Related Work;77
9.4.2;3.4.2. System Model and Problem Statement;78
9.4.2.1;3.4.2.1. Energy and spectral efficiencies;80
9.4.2.2;3.4.2.2. Problem statement;81
9.4.3;3.4.3. Dinkelbach Method;81
9.4.4;3.4.4. Firefly Algorithm;83
9.4.5;3.4.5. Simulations and Numerical Results;84
9.5;3.5. Concluding remarks;89
9.5.1;3.5.1. FA in Traffic Characterization;89
9.5.2;3.5.2. FA in Cooperative Networks;89
9.6;References;89
10;Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation;92
10.1;4.1. Introduction;92
10.2;4.2. Intrusion detection systems;94
10.2.1;4.2.1. IDS Components;95
10.2.2;4.2.2. Research Areas and Challenges in Intrusion Detection;97
10.3;4.3. The method: evolutionary computation;98
10.4;4.4. Evolutionary computation applications on intrusion detection;99
10.4.1;4.4.1. Foundations;99
10.4.2;4.4.2. Data Collection;100
10.4.3;4.4.3. Detection Techniques and Response;102
10.4.3.1;4.4.3.1. Intrusion detection on conventional networks;102
10.4.3.2;4.4.3.2. Intrusion detection on wireless and resource-constrained networks;104
10.4.4;4.4.4. IDS Architecture;105
10.4.5;4.4.5. IDS Security;107
10.4.6;4.4.6. Testing and Evaluation;107
10.5;4.5. Conclusion and future directions;108
10.6;References;110
11;Chapter 5: VoIP Quality Prediction Model by Bio-Inspired Methods;114
11.1;5.1. Introduction;115
11.2;5.2. Speech quality measurement background;116
11.2.1;5.2.1. Subjective Methods;116
11.2.2;5.2.2. Intrusive Objective Methods;117
11.2.3;5.2.3. Nonintrusive Objective Methods;118
11.2.4;5.2.4. Bio-inspired Methods;119
11.3;5.3. Modeling methods;119
11.3.1;5.3.1. Methodology for Conversational Quality Prediction (PESQ/E-model);119
11.3.1.1;5.3.1.1. Basic signal-to-noise ratio, R0;120
11.3.1.2;5.3.1.2. Delay impairment factor, Id;121
11.3.1.3;5.3.1.3. MOS-to-R conversion function and effective equipment impairment factor, Ie-eff;122
11.3.2;5.3.2. Nonlinear Surface Regression Model;123
11.3.3;5.3.3. Neural Network Model;124
11.3.4;5.3.4. REPTree Model;124
11.4;5.4. Experimental testbed;125
11.4.1;5.4.1. The Data Sets' Structure;127
11.4.1.1;5.4.1.1. Codec impairment data set;127
11.4.1.2;5.4.1.2. Human impairment data set;128
11.4.1.3;5.4.1.3. Mixed impairment data set;128
11.4.2;5.4.2. The Performance Measures;129
11.5;5.5. Results and discussion;129
11.5.1;5.5.1. Correlation Comparison;129
11.5.2;5.5.2. Residual Analysis;130
11.6;5.6. Conclusions;132
11.7;References;134
12;Chapter 6: On the Impact of the Differential Evolution Parameters in the Solution of the Survivable Virtual Topology-Mapp...;136
12.1;6.1. Introduction;136
12.2;6.2. Problem Formulation;139
12.3;6.3. DE Algorithm;140
12.3.1;6.3.1. Fitness of an Individual;142
12.3.2;6.3.2. Pseudocode of the DE Algorithm;142
12.3.3;6.3.3. Enhanced DE-VTM Algorithm;143
12.4;6.4. Illustrative Example;143
12.5;6.5. Results and Discussion;147
12.6;6.6. Conclusions;158
12.7;References;158
13;Chapter 7: Radio Resource Management by Evolutionary Algorithms for 4G LTE-Advanced Networks;160
13.1;7.1. Introduction to radio resource management;161
13.1.1;7.1.1. Frame Structure;162
13.1.2;7.1.2. DL and Uplink;162
13.2;7.2. LTE-A technologies;164
13.2.1;7.2.1. Carrier Aggregation;164
13.2.2;7.2.2. Relay Nodes;164
13.2.3;7.2.3. Femtocell;165
13.2.4;7.2.4. Coordinated Multipoint Transmission;165
13.3;7.3. Self-organization using evolutionary algorithms;166
13.3.1;7.3.1. SON Physical Layer;166
13.3.2;7.3.2. SON MAC Layer;167
13.3.3;7.3.3. SON Network Layer;167
13.3.4;7.3.4. LTE-A Open Research Issues and Challenges;168
13.4;7.4. EAs in LTE-A;169
13.4.1;7.4.1. Network Planning;170
13.4.2;7.4.2. Network Scheduling;171
13.4.3;7.4.3. Energy Efficiency;172
13.4.4;7.4.4. Load Balancing;173
13.4.5;7.4.5. Resource Allocation;174
13.4.5.1;7.4.5.1. Genetic algorithm;174
13.4.5.2;7.4.5.2. Game theory;175
13.5;7.5. Conclusion;180
13.6;References;181
14;Chapter 8: Robust Transmission for Heterogeneous Networks with Cognitive Small Cells;184
14.1;8.1. Introduction;184
14.2;8.2. Spectrum sensing for cognitive radio;186
14.3;8.3. Underlay spectrum sharing;187
14.3.1;8.3.1. Underlay Spectrum Sharing for Heterogeneous Networks with MIMO Channels;188
14.3.2;8.3.2. Underlay Spectrum Sharing for Heterogeneous Networks with Doubly Selective Fading SISO Channels;188
14.4;8.4. System Model;189
14.4.1;8.4.1. System Model with MIMO Channel;189
14.4.2;8.4.2. System Model with Doubly Fading Selective SISO Channel;189
14.5;8.5. Problem Formulation;190
14.6;8.6. Sparsity-enhanced mismatch model (SEMM);192
14.7;8.7. Sparsity-enhanced mismatch model-reverse DPSS (SEMMR);194
14.8;8.8. Precoder design using the SEMM and SEMMR;196
14.8.1;8.8.1. SEMM Precoder Design;196
14.8.2;8.8.2. Second-stage SEMMR Precoder and Decoder Design;197
14.9;8.9. Simulation results;199
14.9.1;8.9.1. SEMM Precoder;199
14.9.2;8.9.2. SEMMR Transceiver;200
14.10;8.10. Conclusion;201
14.11;References;202
15;Chapter 9: Ecologically Inspired Resource Distribution Techniques for Sustainable Communication Networks;204
15.1;9.1. Introduction;204
15.2;9.2. Consumer-Resource Dynamics;205
15.3;9.3. Resource Competition in the NGN;207
15.4;9.4. Conditions for Stability and Coexistence;211
15.5;9.5. Application for LTE Load Balancing;214
15.6;9.6. Validation and Results;216
15.7;9.7. Conclusions;220
15.8;References;220
16;Chapter 10: Multiobjective Optimization in Optical Networks;224
16.1;10.1. Introduction;225
16.1.1;10.1.1. Common Optical Network Problems in a Multiobjective Context;225
16.2;10.2. Multiobjective Optimization;227
16.2.1;10.2.1. Multiobjective Optimization Formulation;227
16.2.2;10.2.2. Multiobjective Performance Metrics;228
16.2.3;10.2.3. Experimental Methodology;229
16.2.4;10.2.4. Algorithms to Solve MOPs;231
16.2.4.1;10.2.4.1. Types of optimization problems and WDM networks;231
16.2.4.2;10.2.4.2. Evolutionary algorithms;232
16.2.4.3;10.2.4.3. Ant colony optimization;233
16.3;10.3. RWA Problem;234
16.3.1;10.3.1. Traditional RWA;234
16.3.2;10.3.2. Multiobjective RWA Formulation;235
16.3.3;10.3.3. ACO for RWA;235
16.3.4;10.3.4. MOACO for RWA;236
16.3.5;10.3.5. Classical Heuristics;239
16.3.6;10.3.6. Simulations;240
16.3.7;10.3.7. Experimental Results;241
16.4;10.4. WCA Problem;243
16.4.1;10.4.1. Related Work;244
16.4.2;10.4.2. Classical Problem Formulation;245
16.4.3;10.4.3. Multiobjective Formulation;246
16.4.4;10.4.4. Traffic Models and Simulation Algorithm;246
16.4.5;10.4.5. EA for WCA;247
16.4.6;10.4.6. Experimental Results;248
16.4.6.1;10.4.6.1. Numerical results;249
16.5;10.5. p-Cycle Protection;251
16.5.1;10.5.1. Problem Formulation;254
16.5.2;10.5.2. Generating Candidate Cycles;255
16.5.3;10.5.3. Multiobjective Evolutionary Algorithms;256
16.5.4;10.5.4. Experimental Results;257
16.6;10.6. Conclusions;258
16.7;References;259
17;Chapter 11: Cell-Coverage-Area Optimization Based on Particle Swarm Optimization (PSO) for Green Macro Long-Term Evolutio...;264
17.1;11.1. Introduction;264
17.2;11.2. Related works;265
17.3;11.3. Mechanism of proposed cell-switching scheme;267
17.4;11.4. System model and problem formulation;269
17.5;11.5. PSO algorithm;271
17.6;11.6. Simulation results and discussion;273
17.6.1;11.6.1. Simulation Setup;273
17.6.2;11.6.2. Simulation Flow Chart;273
17.6.3;11.6.3. Results and Discussion;274
17.6.4;11.6.4. Energy and OPEX Savings;278
17.7;11.7. Conclusion;279
17.8;References;280
18;Chapter 12: Bio-Inspired Computation for Solving the Optimal Coverage Problem in Wireless Sensor Networks;282
18.1;12.1. Introduction;283
18.2;12.2. Optimal Coverage Problem in WSN;285
18.2.1;12.2.1. Problem Formulation;285
18.2.2;12.2.2. Related Work;287
18.2.3;12.2.3. Bio-Inspired PSO;288
18.3;12.3. BPSO for OCP;288
18.3.1;12.3.1. Solution Representation and Fitness Function;288
18.3.2;12.3.2. Initialization;289
18.3.3;12.3.3. BPSO Operations;290
18.3.4;12.3.4. Maximizing the Disjoint Sets;291
18.4;12.4. Experiments and Comparisons;291
18.4.1;12.4.1. Algorithm Configurations;291
18.4.2;12.4.2. Comparisons with State-of-the-Art Approaches;292
18.4.3;12.4.3. Comparisons with the GA Approach;293
18.4.4;12.4.4. Extensive Experiments on Different Scale Networks;295
18.4.5;12.4.5. Results on Maximizing the Disjoint Sets;297
18.5;12.5. Conclusion;301
18.6;References;302
19;Chapter 13: Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless Mesh Networks;306
19.1;13.1. Introduction;307
19.2;13.2. Problem Formulation;309
19.2.1;13.2.1. System Model;309
19.2.2;13.2.2. Channel Assignment Problem;311
19.2.3;13.2.3. Related Channel Assignment Algorithms;313
19.3;13.3. Clonal-Selection-Based Algorithms for the Channel Assignment Problem;314
19.3.1;13.3.1. Phase One;315
19.3.1.1;13.3.1.1. Initialization;315
19.3.1.2;13.3.1.2. Affinity evaluation;316
19.3.1.3;13.3.1.3. Clonal selection and expansion;318
19.3.1.3.1;13.3.1.3.1. CLONALG;318
19.3.1.3.2;13.3.1.3.2. BCA;319
19.3.1.3.3;13.3.1.3.3. CLIGA;320
19.3.1.4;13.3.1.4. Local search;321
19.3.2;13.3.2. Phase Two;321
19.3.3;13.3.3. Variants of the Channel Assignment Algorithm;322
19.4;13.4. Performance Evaluation;323
19.4.1;13.4.1. Comparison with Other Channel Assignment Algorithms;325
19.4.2;13.4.2. Convergence of IA;327
19.4.3;13.4.3. Impact of Parameter Setting;328
19.4.4;13.4.4. Impact of Local Search;331
19.4.5;13.4.5. Variants of Channel Assignment Algorithm;332
19.5;13.5. Concluding remarks;337
19.6;References;339
20;Index;342

Leseproben

Chapter 2

Bio-Inspired Approaches in Telecommunications

Su Fong Chien1 [e-mail]; 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...