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Comparative study of the performance of mimo equalizers for wireless communication receivers

 

Table Of Contents


Chapter ONE

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms

Chapter TWO

2.1 Overview of Wireless Communication Receivers
2.2 Evolution of MIMO Technology
2.3 MIMO Equalizers in Wireless Communication
2.4 Types of MIMO Equalizers
2.5 Performance Metrics for MIMO Equalizers
2.6 Challenges in MIMO Equalization
2.7 Previous Studies on MIMO Equalizers
2.8 Advances in MIMO Equalization Techniques
2.9 Applications of MIMO Equalizers
2.10 Future Trends in MIMO Equalization

Chapter THREE

3.1 Research Design
3.2 Sampling Methods
3.3 Data Collection Techniques
3.4 Data Analysis Procedures
3.5 Experimental Setup
3.6 Validation of Results
3.7 Ethical Considerations
3.8 Limitations of Methodology

Chapter FOUR

4.1 Analysis of Data Collected
4.2 Comparison of MIMO Equalizer Performance
4.3 Impact of Channel Conditions on Equalization
4.4 Evaluation of Equalization Techniques
4.5 Statistical Interpretation of Results
4.6 Discussion on Findings
4.7 Implications of Results
4.8 Recommendations for Future Research

Chapter FIVE

5.1 Conclusion and Summary
5.2 Recap of Research Objectives
5.3 Key Findings of the Study
5.4 Contributions to the Field
5.5 Practical Implications
5.6 Suggestions for Further Studies

Thesis Abstract

Multiple-Input Multiple-Output (MIMO) equalizers are of enormous importance in wireless communication systems due to their ability to combat the effect of Intersymbol Interference (ISI) in multipath environment. In this research, a MIMO 2X2, 2X3, 2X4, 2×5, 4X4, and 6X6 system transmission with Binary Phase shift keying (BPSK) modulation in Rayleigh fading channel was modeled and simulated using MATLAB® V8.4.0.529 (R2009b) Communication toolbox. The different equalization schemes namely Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML) which mitigated the effect of ISI were compared to analyze the Bit Error Rate (BER) performance of the system. The results showed that the BER decreased as the antenna configuration is increased from 2X2, 4X4, to 6X6 for ML and MMSE case only. For a BER point 10-3 which is the benchmark for voice quality service, the ML equalizer outperformed the MMSE up to 11dB while the MMSE had a better performance over ZF with about 3 dB gain. Also results shows that a constant gain of 11 dB is maintained between ML and MMSE irrespective of the antenna configuration employed. This implied that, there is much reduction in transmitter power requirement when using ML equalizer as compared to MMSE and ZF. This is important with respect to energy savings and cost of radio equipment.

Thesis Overview

INTRODUCTION

 

 

1.1 Background

 

 

Wireless communication is a rapidly growing segment of the communications industry, with the potential to provide high-speed, high-quality information exchange between portable devices across the globe. The dramatic development of wireless communication over the last few decades has been tremendous. In the field of mobile communication, the demand for better technologies has surged, from voice communications requiring a data rate of a few Kbps to mobile ultra-broadband communication with a data rate of 100Mbps (as specified by the International Telecommunication Union (ITU)).

 

High data-rate wireless access is demanded by many broadband applications such as high speed computer networks, virtual navigation tele-medicine, and online education. Traditionally, more bandwidth is required for such higher data-rate transmission. Unfortunately, due to spectral limitations, it becomes impractical or at times very expensive to increase bandwidth (Li et.al., 2002). More so, increasing transmitter power for capacity to support high date-rate transmission is not a solution because mobile and other portable devices require the use of battery power, which is limited (Agrawal et al., 2012). In this case, using multiple transmit and receive antennas to form a Multiple-Input and Multiple-Output (MIMO) system for spectrally efficient transmission is an alternative solution.

 

MIMO systems have been one of the proposed solutions for enhancing spectrum utilization while fulfilling the data-rate required by the future wireless services. A considerable increase in data throughput and link range can be achieved with MIMO system without additional bandwidth or transmit power.


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