Antenna Array Training and Adaptation Techniques in an Unpredictable and Uncontrolled Interference Environment

In this paper, we investigate the performance of array processing technique based on Sample matrix inversion (SMI) and the effects of channel estimation (using various training schemes) to combat intermittent interference. Most current applications of broadband wireless communication systems use short data block lengths. Therefore, antenna weights estimation is usually done only once and these weights are used for the whole packet length. In this work 1 , through simulations we show that with intermittent kind of interference, weights estimation based on Pre-amble or Post-amble only scheme fails to track the suddenly appearing interferers, resulting in a degradation to the output SINR. Two new, training-based channel estimation techniques are presented which show superior performance over the aformentioned training schemes in this type of environment. For simulating intermittent interference traffic streams, a batch poisson traffic model is used.