Efficient blind adaptive channel shortening algorithm for multicarrier modulation systems

New blind, adaptive channel shortening TEQ for multicarrier modulation systems

“…To investigate the effectiveness of MERO in minimising the correlation coefficients, Pearson product moment [11] is used as an error between adjacent samples of TEQ output. Assuming that the mean of y(i) is zero and the standard deviation of vector y is σ y , where y = [y(i), y(i + 1), …, y(i + N + v)] and the adjacent samples standard deviation is also the same (i.e.…”

“…The system model shown in Fig. 1 is the same model as used in [11]. Each of the N symbols, X k , modulates a subcarrier.…”

“…Each of the N symbols, X k , modulates a subcarrier. The modulation is performed using N point inverse fast Fourier transform, and adding CP to produce orthogonal data to avoid ISI and ICI [11]. After the CP is added, the last ν samples are identical to the first ν samples in the symbol.…”

“…SLAM, on the other hand, also uses the AR method which has lower complexity of 4L w ; however, it has the drawback of depending on the previous estimate which makes it good only for slow time varying channel. In MERO algorithm, the computational complexity is calculated by splitting the algorithm equation into the next three segments by approximating the requirement of the multiply-adds of each segment To investigate the effectiveness of MERO in minimising the correlation coefficients, Pearson product moment [11] is used as an error between adjacent samples of TEQ output. Assuming that the mean of y(i) is zero and the standard deviation of vector y is σ y , where y = [y(i), y(i + 1), …, y(i + N + v)] and the adjacent samples standard deviation is also the same (i.e.…”