A blind channel shortening criterion based on high-order cumulants

In this work, we propose a new criterion for blind channel shortening based on high order statistical information. This method overcomes the weakness of two existing channel shortening criteria derived from only second order output statistics of the unknown channel. It is applicable to both single-input-single-output and single-input-multiple-output frequency selective channels. The optimization criterion can be achieved through either a stochastic gradient descent algorithm or a batch algorithm. Simulation results demonstrate successful shortening of wireless channels that the existing criteria fail to shorten.