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Hyper Differential Evolution (H-DE)is a new optimization technique. We develop a novel blind Detection algorithm based on H-DE in conjunction with Independent Component Analysis (ICA). We then apply the algorithm to the multi-user symbol estimation problem in Direct Sequence Code Division Multiple Access (DS-CDMA) systems. Blind detection is to estimate multiple symbol sequences associated with all users in the downlink of the DS-CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. Bit error rate (BER) simulation evidence of the improved performance of this novel algorithm are shown for different number of users, signal to noise ratio (SNR) and different number of symbols per user. The performance is compared with the traditional FAST ICA Detector. The results show that the proposed algorithm performs well and outperforms the FAST ICA Detector in estimating the symbol signals from the mixed CDMA received signals. [ABSTRACT FROM PUBLISHER]

This Paper develops a novel blind Detection algorithm based on Extension of Efficient FAST Independent Component Analysis (EF-ICA). classifying the received data to many parts in order to use a fit non-quadratic function to separate a non-Gaussian source. Blind detection is to estimate multiple symbol sequences associated with all users in the downlink of the DS-CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. Bit error rate (BER) simulation evidence of the improved performance of this novel algorithm are shown for different number of users, signal to noise ratio (SNR) and different number of symbols per user. The performance is compared with several Blind Detectors based on Efficient FastICA and Fast ICA algorithms. The results show that the proposed algorithm performs well and outperforms the other Detectors in estimating the symbol signals from the mixed CDMA received signals. [ABSTRACT FROM PUBLISHER]