Blind, Adaptive Channel Shortening Equalizer Algorithm Which Can Provide Shortened Channel State Information (BACS-SI)

Channel shortening equalization plays an important role in multicarrier modulation (MCM) systems. In this paper, we propose a blind channel shortening equalizer structure named blind, adaptive channel shortening equalizer which can provide the shortened channel state information (BACS-SI). The algorithm depends on the minimization of a cost function defined as the sum-squared difference of the autocorrelations of the shortened channel impulse response (CIR) and a target impulse response. The surface is proven to be multimodal; however, minima are shown to be related to each other in a certain way. A two-phase approach is proposed. In the first phase, the cost function is minimized by a stochastic gradient descent algorithm in order to find an arbitrary minimum. In the second phase using the relation between minima, genetic algorithms are employed to find the best minimum according to a fitness function. The algorithm can both successfully shorten the channel and also explicitly provides shortened CIR which is a necessary information for the proper operation of a MCM receiver, in contrast to many other algorithms proposed in the literature which cannot directly provide this information.