الفهرس 
Basics of Blind Signal Separation (BSS)Only 14 pages are availabe for public view
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Abstract
Blind signal separation (BSS) is studied for the system of multiple input and multiple output (MIMO) signals. Blind signal separation (BSS) has gained interest of researchers in various fields of research. The transmitted signal is subject to Additive white gaussian noise (AWGN). The received noisy signals are mixed and corrupted by inter-user interference (IUI), and considered as the outputs of a linear (MIMO) memory-less system. In this thesis, we present three different approaches to solve the BSS problem. The first approach is based on investigating the performance of different existing BSS algorithms on different modulation techniques such as quadrature phase shift keying (QPSK), minimum shift keying (MSK), and Gaussian minimum shift keying (GMSK). The performances of the principle component analysis (PCA) algorithm as a second order statistics separation algorithm and the independent component analysis (ICA), and multi user kurtosis (MUK) algorithms as higher order statistics algorithms are studied and compared with different modulation techniques. The second approach adopts wavelet denoising as a pre-processing step prior to separation. The third approach is based on signal separation in the DCT domain to make use of the energy compaction property of the DCT.