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

 

Table Of Contents


Chapter ONE

INTRODUCTION

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

Chapter TWO

LITERATURE REVIEW

  • 2.1Overview of Wireless Communication Systems
  • 2.2Evolution of MIMO Technology
  • 2.3Principles of MIMO Equalizers
  • 2.4Types of MIMO Equalizers
  • 2.5Performance Metrics for Wireless Communication Receivers
  • 2.6Previous Studies on MIMO Equalizers
  • 2.7Challenges in Implementing MIMO Equalizers
  • 2.8Applications of MIMO Equalizers
  • 2.9Future Trends in MIMO Equalizers
  • 2.10Gaps in Existing Literature

Chapter THREE

SYSTEM DESIGN AND IMPLEMENTATION

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

Chapter FOUR

SYSTEM TESTING AND EVALUATION

  • 4.1Performance Evaluation of MIMO Equalizers
  • 4.2Simulation Environment
  • 4.3Comparative Analysis of Different MIMO Equalizers
  • 4.4Impact of Channel Conditions on Equalizer Performance
  • 4.5Computational Complexity of MIMO Equalizers
  • 4.6Trade-offs in Equalizer Design
  • 4.7Real-world Implementation Challenges
  • 4.8Recommendations for Improving Equalizer Performance

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

  • 5.1Summary of Findings
  • 5.2Conclusions
  • 5.3Implications of the Study
  • 5.4Contributions to Knowledge
  • 5.5Recommendations for Future Research
  • 5.6Practical Applications of the Study
  • 5.7Limitations of the Study
  • 5.8Conclusion Remarks

Project Abstract

<p> 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 <strong>MATLAB® V8.4.0.529 (R2009b)</strong>&nbsp;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. <br></p>

Project Overview

<p> </p><p><strong>INTRODUCTION</strong></p><p>&nbsp;</p><p>&nbsp;</p><p><strong>1.1 Background</strong></p><p>&nbsp;</p><p>&nbsp;</p><p>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)).</p><p>&nbsp;</p><p>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.</p><p>&nbsp;</p><p>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.</p> <br><p></p>

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