Blind adaptive channel shortening by single lag autocorrelation minimisation

A blind adaptive channel shortening algorithm based on minimising the sum of the squared autocorrelations (SAM) of the effective channel was recently proposed. We submit that essentially identical channel shortening can be achieved by minimising the square of only a single autocorrelation. Our proposed single lag autocorrelation minimisation (SLAM) algorithm has very low complexity. We also constrain the autocorrelation minimisation with a novel stopping criterion so that the shortening signal-to-noise ratio (SSNR) of the effective channel is not minimised by the autocorrelation minimisation. The simulations show that SLAM achieves higher bit rates than SAM.