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Title of Thesis

Applications of Particle Swarm Optimization to Digital Communication

Author(s)

Muhammad Zubair

Institute/University/Department Details
Department of Electronic Engineering, Faculty of Engineering and Technology / International Islamic University, Islamabad
Session
2008
Subject
Electronic Engineering
Number of Pages
173
Keywords (Extracted from title, table of contents and abstract of thesis)
Applications, Particle, Swarm, Optimization, Digital, Communication, robustness, optimization, algorithms, PSO

Abstract
The whole class of evolutionary computing algorithms is inspired by the process of evolution in nature. Compared to the traditional optimization algorithms, a few striking features of these algorithms include their ability to address non-differentiable cost functions, robustness to the dynamically changing environment, and implementation on parallel machines. However, it was not until one and half decade ago, when these algorithms attracted researchers and got acknowledgement in terms of their application to the real world problems. The main reason behind this increased interest of the researchers owes to the ever increasing computing power. As a result evolutionary computing algorithms have been widely investigated and successfully applied for a number of problems belonging to diverse areas. In this dissertation the standard binary particle swarm optimization (PSO) and its soft version, namely soft PSO (SPSO) have been
applied to four different problems of digital communication.
Due to the exponentially growing computational complexity with the number of users in optimum maximum likelihood detector (OMLD), suboptimum techniques have received significant attention. We have proposed the SPSO for the multiuser detection (MUD) in synchronous as well as asynchronous multicarrier code division multiple access (MCCDMA) systems. The performance of SPSO based MUD has been investigated to be near optimum, while its computational complexity is far less than OMLD.
Particle swarm optimization (PSO) aided with radial basis functions (RBF) has been suggested to carry out multiuser detection (MUD) for synchronous direct sequence code division multiple access (DS-CDMA) systems. The MUD problem has been taken as a pattern classification problem and radial basis functions have been used due to their excellent performance for pattern classification.
The two variants of PSO have also been used in a joint manner for the task of the channel and data estimation based on the maximum likelihood principle. The PSO algorithm works at two different levels. At the upper level the continuous PSO estimates the channel, while at the lower level, the soft PSO detects the data. The simulation results have proved to be better than that of joint Genetic algorithm and Viterbi algorithm (GAVA) approach.

Download Full Thesis
751 KB
S. No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

xv
20 KB
2

1

INTRODUCTION

1.1 Multiple Access Techniques
1.2 Evolutionary Algorithms
1.3 Contributions of Dissertation
1.4 Organization of Dissertation

1
24 KB
3 2 PARTICLE SWARM OPTIMIZATION

2.1 Introduction
2.2 Stochastic Algorithms
2.3 Evolutionary Algorithms
2.4 Particle Swarm Optimization

6
137 KB
4 3 PSO ASSISTED MULTIUSER DETECTION FOR MC-CDMA

3.1 Introduction
3.2 Background
3.3 Problem formulation of MUD of Synchronous MC-CDMA system
3.4 Problem Formulation of MUD of Asynchronous MC-CDMA System

39
240 KB
5 4 RADIAL BASIS FUNCTIONS AND PSO ASSISTED MULTIUSER DETECTION FOR DS-CDMA

4.1 Introduction
4.2 Background
4.3 Problem Formulation of RBF assisted MUD for Synchronous DS-CDMA System

75
106 KB
6 5 JOINT CHANNEL AND DATA ESTIMATION USING PARTICLE SWARM OPTIMIZATION

5.1 Introduction
5.2 Background
5.3 Problem Formulation
5.4 PSO for Joint Channel and Data Estimation
5.5 Simulation Results and Discussion

88
96 KB
7 6 CONCLUSION AND FUTURE WORK

6.1 Conclusion
6.2 Future Work

101
20 KB
8

7

REFERENCES 104
187 KB