After the receive filter, an adaptive equalizer is employed to recover the transmitted data in the presence of channel distortion. The FIR adaptive equalizer are built from a Walsh network and an LMS algorithm. The Walsh network is employed as an adap...
After the receive filter, an adaptive equalizer is employed to recover the transmitted data in the presence of channel distortion. The FIR adaptive equalizer are built from a Walsh network and an LMS algorithm. The Walsh network is employed as an adaptive equalizer for which an algorithm for recursively adjusting the tap gain coefficients minimizing the mean square error. The Walsh network consists of a Walsh and Block pulse functions. In this paper, minimization of the mean square error is accomplished by the now well-known LMS algorithm. In the LMS algorithm, the convergence factor is an important design parameter because it governs stability and convergence speed, which depend on the proper choice of the convergence factor. The conventional adaptation techniques use a fixed time constant convergence factor by the method of trial and error.
In this paper, we proposed an optimal method in the choice of the convergence factor.