Bursty Mixed Gaussian-Impulsive Noise Model and Parameter Estimation

In many communication scenarios, the communication signals are contaminated by both Non-Gaussian impulsive noise (IN) with memory and white Gaussian noise (WGN), i.e., bursty mixed Gaussian-impulsive noise. To achieve desirable communication performance, it is necessary to establish an accurate and tractable model of the bursty mixed noise. In this paper, we propose a closed-form and heavy-tail multivariate probability density function (PDF) to model the bursty mixed noise. The proposed model is the weighted combination of the Gaussian distribution and student distribution that separately describe the background WGN and the bursty IN. We design the model parameters estimation approach based on the empirical characteristic function of the proposed noise model. The performances of parameter estimation are also analyzed. Numerical results show that our proposed bursty mixed noise model can well match the measured noise in practical scenarios. Meanwhile, the estimation performances in terms of normalized mean square error (NMSE) verify the effectiveness of presented parameters estimation methods.