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 MIMO technology
- 2.2Evolution of MIMO equalizers
- 2.3Types of MIMO equalizers
- 2.4Performance metrics for MIMO systems
- 2.5MIMO equalizer design considerations
- 2.6Challenges in MIMO equalization
- 2.7Comparative analysis of MIMO equalizers
- 2.8Case studies of MIMO equalizer implementations
- 2.9Future trends in MIMO equalization
- 2.10Summary of literature review
Chapter THREE
SYSTEM DESIGN AND IMPLEMENTATION
- 3.1Research methodology overview
- 3.2Research design and approach
- 3.3Data collection methods
- 3.4Sampling techniques
- 3.5Experimental setup
- 3.6Data analysis techniques
- 3.7Validity and reliability
- 3.8Ethical considerations
Chapter FOUR
SYSTEM TESTING AND EVALUATION
- 4.1Data presentation and analysis
- 4.2Performance evaluation of MIMO equalizers
- 4.3Comparison of simulation results
- 4.4Impact of channel conditions on equalizer performance
- 4.5Effect of noise and interference
- 4.6Robustness of MIMO equalizers
- 4.7Discussion on implementation challenges
- 4.8Recommendations for improvement
Chapter FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
- 5.1Summary of findings
- 5.2Conclusion
- 5.3Contributions to the field
- 5.4Implications for future research
- 5.5Limitations of the study
- 5.6Recommendations for further study
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> 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
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</p><p><strong>INTRODUCTION</strong></p><p> </p><p> </p><p><strong>1.1 Background</strong></p><p> </p><p> </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> </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> </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>
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