Performance Of Independent Component Analysis Algorithms Research Articles

BER Performance Improvement of Alamouti MIMO-STBC Decoder Using Mutual Information Method

Bit error rate (BER) performance of multiple input multiple output (MIMO) space time block code (STBC) decoder depends on the channel estimator quality, where the quality of classical least square (LS) estimator depends mainly on the number of pilot symbols. The aims of this paper are to implement a new STBC decoder, measure the BER performance and make comparison with conventional one, without increasing the number of pilot symbols. Although the new strategy is more complex than a conventional decoder, it gives the opportunity to join estimation and decoding at the same time. Minimum mutual information (MMI)- independent component analysis (ICA) algorithm has been used for extracting source signals. MIMO-STBC systems were taken as a cases study with two transmitters. The simulation results show that MMI-ICA algorithm provides superior BER performance as compared with conventional maximum ratio combiner (MRC). The important point, which denoted during simulation, the performance of ICA algorithm with high frame length provides better BER performance than the conventional one.

Evaluating model misspecification in independent component analysis

Independent component analysis (ICA) is a popular blind source separation technique used in many scientific disciplines. Current ICA approaches have focused on developing efficient algorithms under specific ICA models, such as instantaneous or convolutive mixing conditions, intrinsically assuming temporal independence or autocorrelation of the sources. In practice, the true model is not known and different ICA algorithms can produce very different results. Although it is critical to choose an ICA model, there has not been enough research done on evaluating mixing models and assumptions, and how the associated algorithms may perform under different scenarios. In this paper, we investigate the performance of multiple ICA algorithms under various mixing conditions. We also propose a convolutive ICA algorithm for echoic mixing cases. Our simulation studies show that the performance of ICA algorithms is highly dependent on mixing conditions and temporal independence of the sources. Most instantaneous ICA algorithms fail to separate autocorrelated sources, while convolutive ICA algorithms depend highly on the model specification and approximation accuracy of unmixing filters.