Adaptive Second-Order Asynchronous CCI Cancellation: Maximum Likelihood Benchmark for Regularized Semi-Blind Technique

An asynchronous interference cancellation problem is addressed when the training and working intervals are available containing the desired signal and arbitrary ovelapping interference. Likelihood ratio (LR) maximization approach for estimation of the structured correlation matrices over both training and working intervals is developed for the Gaussian data model and exploited to obtain a benchmark performance 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 according to the developed LR maximization technique.