A robust blind adaptive multiuser detection based on maximum correntropy criterion in satellite CDMA systems

Abstract

Satellite code‐division multiple‐access systems use multiuser detection (MUD) methods to deal with multiple‐access interferences. Among the different kinds of MUD, blind adaptive MUD regarding the system as a state‐space model is widely used for its tracking capability and independence of training sequences. The innovation process noise of the state‐space model is usually assumed to follow the Gaussian distribution, and the minimum mean‐square error criterion is the default criterion for most conventional blind adaptive MUD methods. In the past few years, the impulsive noise of the innovation process has attracted attention, declaring that a Gaussian distribution is not suitable for simulating the impulsive noise. Thus, more criteria are proposed to enhance the robustness of the blind adaptive MUD. This paper improves the robustness of MUD methods under impulsive noise by bringing forward a new cost function based on the maximum correntropy criterion and deduces the expression of the blind adaptive detector. Simulation results show that the proposed method achieves superior performance in terms of bit error rate, convergence, and robustness compared with other methods.

An Adaptive Channel Estimation Based on Fixed-Point Generalized Maximum Correntropy Criterion

“…The robust cost function is not limited to the least p-norm criterion, and optimization criteria enlightened by information-theoretic learning (ITL) are also effective in dealing with the impulsive noises. With the development of the information theoretic learning research [17], [18], correntropy which measures the similarity of two random variables has appeared on the perspective of scholars [11], [18]- [22], and maximum correntropy criterion(MCC) has been adopted in robust adaptive algorithms [23]- [28]. Among these researches, [23] proposed a robust sparse adaptive filtering algorithm for channel estimation utilizing correntropy induced metric (CIM) penalized MCC and proved that MCC was effective in mitigating the influence of impulsive noises.…”

Newtonian-Type Adaptive Filtering Based on the Maximum Correntropy Criterion

“…A popular robust solution is to change the MMSE criterion to other criteria that suppress impulsive noise, such as Huber loss [ 8 ], least p-norm criterion [ 9 ], etc. In recent years, information theory learning (ITL) was found suitable to deal with non-Gaussian noises [ 10 , 11 , 12 , 13 , 14 ]. Inspired by ITL, maximum correntropy criterion (MCC) or generalized maximum correntropy criterion (GMCC)-based adaptive filtering was studied [ 15 , 16 , 17 , 18 , 19 , 20 ].…”