Adaptive second-order asynchronous CCI cancellation: maximum likelihood benchmark for regularized semi-blind technique

An asynchronous interference cancellation problem is addressed when training and working intervals are available, containing the desired signal and arbitrary overlapping interference. A likelihood ratio (LR) maximization approach is developed for estimation of the structured correlation matrices over both training and working intervals for the Gaussian data model and exploited to obtain a performance benchmark for ad-hoc estimators. A regularized non-iterative estimation of the antenna array coefficients is proposed, which employs the autocorrelation matrix estimation as a weighted sum of the autocorrelation matrices estimated over the training and working intervals. It is shown by means of simulation in TDMA and OFDM environments that the regularized semi-blind solution significantly outperforms the conventional estimators and demonstrates performance close to the LR based benchmark.