Robust multiuser detection in impulsive channels based on M-estimation using a new penalty function

Introduction: Detection and estimation theory is one of the core investigation areas in communications. Multiuser detection (MUD) is an estimation problem in which different users transmit their signals through the same channel in a multiple access channel (MAC). In this way the receiver explores desired signal through the ambient noise and interference generated by other user signals. Multiuser detection refers to detection techniques that aim at estimation of the desired signal in this circumstance. The maximum likelihood (ML) multiuser detection studied by Verdu [1], but the computational complexity of the ML detector exponentially increases by the number of users, hence using suboptimal detectors is of interest. The majority of references has shown that in many cases the ambient noise model does not follow the Gaussian assumption [2, 3]. In this case the Gaussian assumption will cause a great performance loss in receiver [3]. On the other hand non-Gaussian noise distribution has a parametric model where estimation of these parameters is practically impossible. The problem of multiuser detection in non-Gaussian channels have been the subject of many references. Wang and Poor introduced a robust multiuser detection [2] based on Huber’s robust Minimax detector [4]. They used some approximation to make the Minimax detector practical in non-Gaussian environment. Seyfe and Valaee [5] introduced a penalty function for robust multiuser detection, which outperforms the Minimax detector. In [6] and [7] Seyfe and Sharafat used Nonparametric statistics to introduce the Nonparametric multiuser detection. In the next sections we introduce our new penalty function, we analyze the performance of this detector in different non-Gaussian environments and finally the simulation result shows the performance of this detector compares to other detectors.