Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method

TL;DR

This paper introduces a U-net++ based neural network approach for estimating parameters of mixed Gaussian-impulsive noise in communication systems, improving accuracy and robustness over existing methods.

Contribution

The paper proposes a novel neural network-based method using U-net++ for separating mixed noise and estimating its parameters from single-channel signals.

Findings

Better estimation accuracy than existing blind source separation methods.

Enhanced robustness across various noise scenarios.

Effective separation of mixed Gaussian-impulsive noise.

Abstract

In many scenarios, the communication system suffers from both Gaussian white noise and non-Gaussian impulsive noise. In order to design optimal signal detection method, it is necessary to estimate the parameters of mixed Gaussian-impulsive noise. Even though this issue can be well tackled with respect to pure mixed noise, it is quite challenging based on the received single-channel signal including both transmitting signal and mixed noise. To mitigate the negative impact of transmitting signal, we propose a parameter estimation method by utilizing a neural network, namely U-net++, to separate the mixed noise from the received single-channel signal. Compared with existing blind source separation based methods, simulation results show that our proposed method can obtain rather better performance in terms of estimation accuracy and robustness under various scenarios.