A novel single lag auto-correlation minimization (SLAM) algorithm for blind adaptive channel shortening

A blind adaptive channel shortening algorithm based on minimizing the sum of the squared auto-correlations (SAM) of the effective channel was recently proposed. We submit that identical channel shortening can be achieved by minimizing the square of only a single auto-correlation. Our proposed single lag auto-correlation minimization (SLAM) algorithm has, therefore, very low complexity and also it does not require, a priori, knowledge of the length of the channel. We also constrain the auto-correlation minimization with a novel stopping criterion so that the shortening signal-to-noise ratio (SSNR) of the effective channel is not minimized by the auto-correlation minimization. Simulations have shown that SLAM achieves higher bit rates than SAM.