On the Performance of Space-Time Block Coding Based on ICA Neural Networks

For conventional space-time block coding (STBC), the decoding usually requires accurate channel state estimation. However, the accuracy of the channel estimation strongly determines the system performance. Independent component analysis (ICA) techniques can be applied to perform blind detection so as to detect the transmitted symbols without any channel information. In this paper, we establish the special ICA model for the STBC system and study the performance of STBC schemes based on ICA neural networks; what is more, several different ICA algorithms of blind separation are used for performance evaluation. By using the ICA based schemes, the good robustness against channel estimation errors and time variation of the fading channels can be acquired. The computer simulation analyzes the bit error rate (BER) performance of these methods and indicates the optimal separation algorithm suitable for STBC scheme.