Modeling Bursty Mixed Gaussian-Impulsive Noise and its Applications in Demodulation

Bursty mixed noise composed of impulsive noise with memory and white gaussian noise exists in many practical applications. However, existing signal demodulation algorithms are designed without considering the characteristics of the bursty mixed noise, resulting in performance degradation. Therefore, an accurate model for the bursty mixed noise needs to be built to design the corresponding signal demodulation algorithms. In this paper, we propose a novel statistical model based on the mul-tivariate student distribution for the bursty mixed noise, based on which we design the maximum likelihood (ML) demodulation method and analyze the theoretical bit error rate performance. Simulation results show that the proposed model can describe the bursty noise well and there is remarkable demodulation gain of the ML method as compared with conventional ones.