In this paper, a simple and efficient low complexity fast converging partial update normalized LMS (PNLMS) algorithm is proposed for the decision feedback equalization. The proposed implementation is suitable for applications requiring long adaptive equalizers, as is the case in several high-speed wireless communication systems. The proposed algorithm yields good bit error rate performance over a reasonable signal to noise ratio. In each iteration, without reducing the order of the filter, only a part of the filter coefficients are updated so that it reduces the computational complexity and improves speed of operation. The NLMS algorithm can be considered as a special case and slightly improved version of the LMS algorithm which takes into account the variation in the signal level at the filter output and selects a normalized step size parameter which results in a stable as well as fast converging adaptive algorithm. The frequency domain representation facilitates, easier to choose step size with which the proposed algorithm convergent in the mean squared sense, whereas in the time domain it requires the information on the largest eigen value of the correlation matrix of the input sequence. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of convergence rate.

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Adaptive filtering Bit error rate(BER) Mean Square error (MSE) Normalized least mean square (NLMS) algorithm