A new adaptive neural network multiuser detector in synchronous CDMA systems

A new adaptive neural network multiuser detector is proposed and investigated for synchronous code-division multiple-access (CDMA) systems. The proposed multiuser detector includes two parts: a decorrelating detector as its auxiliary detector and an adaptive multiple layer perceptron (MLP) detector as its main detector. At the setup stage, the auxiliary detector detects the user´s transmitted data and at the same time feeds these output data to the main MLP detector as its training data, and the main detector is trained by using the well-known backpropagation (BP) algorithm. After the training process, the auxiliary detector stops work and the main detector starts detecting the user´s transmitted data self-adaptively. The proposed detector is blind and can provide near minimum bit-error-rate performance