2024/02/09 by Tianfu Qi, Jun Wang, Qi, Tianfu +1
Engineering · #FOS: Electrical engineering #Power Line Communications and Noise #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.06395
openalex publication_date 2024/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Non-Gaussian impulsive noise (IN) with memory exists in many practical applications. When it is mixed with white Gaussian noise (WGN), the resultant mixed noise will be bursty. The performance of communication systems will degrade significantly under bursty mixed noise if the bursty characteristic is ignored. A proper model for the bursty mixed noise and corresponding algorithms needs to be designed to obtain desirable performance but there is no such model reported to the best of our knowledge. The important problem is addressed in the two-part paper. In the first part, we propose a closed-form heavy-tailed multivariate probability density function (PDF) that to model the bursty mixed noise. This model is the weighted addition of gaussian distribution and student distribution. Then, we present the parameter estimation method based on the empirical characteristic function of the proposed model and analyze the performance of the parameter estimation. Numerical results show that our proposed bursty mixed noise model matches the measured bursty noise well. Meanwhile, the parameters of the proposed noise model can be accurately estimated in terms of mean square error (MSE).