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

We propose a new blind, adaptive channel shortening algorithm for updating the coefficients of a time-domain equalizer in a system employing multicarrier modulation. The technique attempts to minimize the sumsquared auto-correlation terms of the effective channel impulse response outside a window of desired length. The proposed algorithm, known as "sum-squared auto-correlation minimization" (SAM), requires the source sequence to be zero-mean, white, and wide-sense stationary, and it is implemented as a stochastic gradient descent algorithm. Simulation results are provided, demonstrating the success of the SAM algorithm in an asymmetric digital subscriber loop (ADSL) system.