Multi-user detection based on improved-KICA

In this paper Improved kernel independent component analysis (KICA) algorithm is proposed for detection of direct sequence code division multiple access (DS-CDMA) signals and compared with KICA and FastICA algorithms. ICA based technique is based on independence of source signals and these conditions are satisfied in multi-user CDMA environment. The aim is to recover a set of unknown mutually independent source signals from their observed mixtures without knowledge of the mixing coefficients. KICA which is advanced recently is a non-linear method for blind source separation (BSS). Combining a KICA element to conventional signal detection reduces multiple access interference (MAI) and enables a robust, computationally efficient structure. For traditional KICA algorithm is influenced by kernel function and also ignores noise, in this paper Improved-KICA algorithm using optimal kernel function and considering the noise is proposed for multi-user DS-CDMA signal. In this paper bit error rate simulations of these algorithms has been given for different number of users, SNR and compared. The results show that the proposed Improved-KICA is more effective compared with traditional algorithms and performs better at separating the source signals from the mixed CDMA signals with noise.