Blind, adaptive channel shortening by sum-squared auto-correlation minimization (SAM)

We propose a new blind, adaptive channel shortening algorithm for updating a time-domain equalizer (TEQ) in a system employing multicarrier modulation. The technique attempts to minimize the sum-squared auto-correlation of the combined channel-TEQ impulse response outside a window of desired length. The proposed algorithm, sum-squared auto-correlation minimization (SAM), assumes the source sequence to be white and wide-sense stationary, and it is implemented as a stochastic gradient descent algorithm. Simulation results demonstrating the success of the SAM algorithm are provided.