Block and Partial Update Sign Normalized LMS Based Adaptive Decision Feedback Equalization

In this paper, a simple and efficient Block and partial update sign normalized LMS (BPSNLMS) 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 this scheme the incoming data is partitioned into non overlapping blocks and the filtering operation has been performed in frequency domain with FFT (overlap and save method). In each iteration, only a part of the filter coefficients are updated so that it reduces the computational complexity and improves speed of operation. The signed versions of LMS algorithm makes equalization techniques multiplication free and facilitates efficient digital implementation using shift registers. 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.