Performance Evaluation of Adaptive Arrays for MIMO Smart Antenna Systems

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Addis Ababa University


The demand for wireless systems has been growing rapidly over the recent years due to improved reliability, high data rates, seamless connectivity and low deployment costs. MIMO systems are the most efficient leading innovation of wireless systems for maximum capacity and improved quality and coverage. This theory has been around for a long while but the complexity involved and the signal processing required has been a major drawback to its widespread use. However, recent improvements in Digital Signal Processing (DSP) technology has made it possible to now construct such transmission systems. In this thesis we study different adaptive blind and nonblind algorithms for MIMO systems such as LMS, CMA, SMI, and combined algorithms, LMS-CMA, and SMI-CMA. Moreover, we compare these adaptive array algorithms with other known class of MIMO linear receiver (channel estimation) techniques like Zeroforcing (ZF) and minimum mean square error (MMSE) methods. In addition to this, we have discussed Capacity of MIMO systems and different MIMO transmission techniques such as spatial diversity (SD), Spatial multiplexing(SM). The results of performance evaluation for Adaptive array MIMO receivers revealed that LMS has better BER performance than SMI, SMI-CMA, and ZF and the same performance with MMSE with no need of CSI. LMS algorithm has slow convergence but low complexity compared to MMSE algorithm that has fast convergence with very high complexity. Moreover, the number of training signals can minimized by 62.5% at the cost of 2-4dB SNR using nonblind algorithm( LMS) combined with blind algorithm( CMA). Keywords: Adaptive arrays, MIMO systems, MIMO receivers, blind algorithms, nonblind algorithms, LMS



Adaptive arrays, MIMO systems, MIMO receivers, Blind algorithms, Nonblind algorithms, Lml