Nonminimum phase channel deconvolution using the complex cepstrum of the cyclic autocorrelation

New discrete time blind deconvolution methods are proposed for nonminimum phase linear channels driven by cyclo-stationary inputs. The methods rely exclusively on second-order statistics and do not impose any constraints on the distribution of the channel input as in the case of methods based on higher-order statistics. The output of the channel is fractionally sampled and then the complex cepstrum of the cyclic autocorrelation is obtained. It is shown that this complex cepstrum preserves nonminimum phase information and thus the identification of nonminimum phase channels is possible. Practical constraints in the implementation of the methods and channel identifiability conditions are discussed. The applicability of the methods to both channel identification and fractionally spaced linear and DFE equalization is described and verified by means of computer simulations