Performance analysis of recurrent neural networks based blind adaptive multiuser detection in asynchronous DS-CDMA systems

With dynamics property and highly parallel mechanism, recurrent neural networks (RNN) can effectively implement blind adaptive multiuser detection at the circuit time constant level. In this paper, the RNN based blind adaptive multiuser detection is extended to ubiquitous asynchronous DS-CDMA systems, and the performance of the output signal to interference plus noise ratio, asymptotic multiuser efficiency, computational complexity, operating time, and mismatch of the detector are quantitatively analyzed. With performance analysis and numerical simulations, it is shown that RNN based blind adaptive multiuser detection can converge at the steady quickly and offer significant performance improvement over some existing popular detectors in eliminating multiple access interference and "near-far" resistance.