A new adaptive linear multiuser detector based on approximate negentropy minimization

In this paper, we introduce an information theoretic learning method as a new approach to multiuser detection. We propose a new adaptive linear multiuser detector based on approximate negentropy minimization of the output error and investigate its characteristics and performance. Negentropy includes higher order statistical information and its minimization provides improved converge and performance compared to traditional methods such as minimum mean squared error. The proposed algorithm is derived under the assumption that a Gaussian variable has the largest entropy among all random variables of unit variance and hence a normalization process is required. Simulation experiments show that our multiuser detector has similar bit error rate (BER) characteristics to the least BER multiuser detector. Furthermore, the proposed detector has faster convergence speed than the LBER detector.