On correspondence between training-based and semiblind second-order adaptive techniques for mitigation of synchronous CCI

The synchronous interference cancellation problem is addressed when training and working intervals are available that contain the desired signal and completely overlapping interference. A maximum-likelihood (ML) approach is applied for estimation of the structured covariance matrices over both training and working intervals for a Gaussian data model. It is shown that the efficiency of the ML solution is close to the efficiency of the least-squares (LS) estimator, which means that the conventional training-based LS algorithm practically cannot be improved upon in the class of second-order semiblind techniques under the synchronous interference scenario.