Receiver Design Using Artificial Neural Network for Signal Detection in Multi Carrier – Code Division Multiple Access System

Multi carrier–code division multiple access (MC–CDMA) system is a promising wireless communication technology with high spectral efficiency and system performance. Though the multiple access techniques provide high spectral efficiency, these techniques were prone to multiple access interference (MAI). So, this paper mainly aims at the design of the MC–CDMA receiver to mitigate MAI. The classical receivers like maximal ratio combining (MRC), equal gain combining (EGC), and minimum mean square error (MMSE) fails to cancel MAI when the MC– CDMA is subjected to non-linearistic degradations. By contrast, being highly non-linear classifiers, the neural network (NN) receivers could be better alternative under such a case. The feasibility, efficiency and effectiveness of the proposed multilayer perceptron (MLP) NN based receiver are studied in detail for the MC–CDMA with nonlinearistic degradations.

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