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Title page
Table of Contents
Abbreviations and symbols

Chapter One: Introduction
1.1       Background
1.2       Statement of Problem
1.3       Research Aim and Objective
1.4       Scope and Significance of the Research
1.5       Limitations of the Research

Chapter Two: Literature Review and Theoretical Background
2.1       Introduction
2.2       Review of Prior Works
2.3       Principle of MIMO Transmission system
2.4       Multiplexing Gain
2.5       Diversity Gain
2.6       MIMO information Theory
2.7       MIMO systems Capacity
2.8       Digital Modulation
2.8.1 Phase Shift Keying
2.9       Multipath Propagation
2.10     Rayleigh Fading Channel
2.11     Wideband Fast Fading and its Effect
2.12     Overcoming Wideband Fast Fading
2.13     Equalization
2.13.1 Linear Equalizer
2.13.2 Zero Forcing Equalizer
2.13.3 Mathematical Modeling of ZF equalizer
2.13.4 Minimum Mean Square Error Equalizer
2.13.5 Mathematical Modeling of MMSE Equalizer
2.14     Non- Linear Equalizer
2.14.1 Maximum Likelihood Equalizer
2.14.2 Mathematical Modeling of ML Equalizer
2.14.3 Decision Feedback Equalizer
2.15 Other Equalization Methods
2.16 Conclusion

Chapter Three: Methodology
3.1 Introduction
3.2 Choice of Modulation Scheme
3.3 MIMO system Model
3.4 Simulation to determine the performance of MIMO Equalizers
3.4.1 Data Generation
3.4.2 Modulation
3.4.3 Grouping
3.4.4 Transmission Channel
3.4.5 Equalization
3.4.6 Quantization
3.4.7 Regrouping/Reshaping
3.4.8 Demodulation
3.4.8 Bit Error Rate Calculation
3.5 MIMO system with ZF Equalizer
3.6 MIMO system with MMSE Equalizer
3.7 MIMO system with ML Equalizer
3.8 Conclusion

Chapter Four: Results and Discussion
4.1 Introduction
4.2 Performance Measure
4.3 MIMO with ZF Equalizer
4.4 MIMO with MMSE Equalizer
4.5 MIMO with ML Equalizer
4.6 Comparing the Performance of ZF MMSE ML Equalizers
4.6 Proposed Validation
4.7 Conclusion

Chapter Five: Conclusion and Recommendation for Further Work
5.1 Introduction
5.3 Conclusion
5.4 Recommendation for Further work


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, 2x5, 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.



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, 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. MIMO system achieves this by higher spectral efficiency (more bits per second per Hertz of.......

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