Separation Performance of Ica Algorithms in Communication Systems

In commercial cellular networks, like the systems based on direct sequence code division multiple access (DSCDMA), many types of interferences can appear, starting from multi-user interference inside each sector in a cell to interoperator interference. Also unintentional jamming can be present due to co-existing systems at the same band, whereas intentional jamming arises mainly in military applications. Independent Component Analysis (ICA) use as an advanced pre-processing tool for blind suppression of interfering signals in direct sequence spread spectrum communication systems utilizing antenna arrays. The role of ICA is to provide an interference-mitigated signal to the conventional detection. Several ICA algorithms exist for performing Blind Source Separation (BSS). ICA has been used to extract interference signals, but very less literature is available on the performance, i.e., how does it behave in communication environment. This needs an evaluation of its performance in communication environment. This paper evaluates the performance of some major ICA algorithms like Bell and Sejnowski\u27s infomax algorithm, Cardoso\u27s joint approximate diagonalization of eigen matrices (JADE) algorithm, Hyvarinen\u27s fixed point algorithm, Pearson-ICA algorithm and Comon\u27s algorithm in a communication blind source separation problem. Independent signals representing sub-Gaussian, Gaussian and mix users(sub-Gaussian, super-Gaussian and Gaussian) are generated and then mixed linearly to simulate communication signals. Separation performance of ICA algorithms measure by performance index