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.