论文标题
混合高斯冲动噪声的参数估计：基于U-NET ++的方法
Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method
论文作者
论文摘要
在许多情况下，通信系统都患有高斯白噪声和非高斯冲动噪声。为了设计最佳信号检测方法，有必要估算混合高斯冲动噪声的参数。即使在纯混合噪声方面可以很好地解决此问题，但基于接收到的单通道信号，包括传输信号和混合噪声，这也很具有挑战性。为了减轻传输信号的负面影响，我们通过利用神经网络（即U-net ++）提出了一种参数估计方法，将混合噪声与接收到的单通道信号分开。与现有的基于盲源分离的方法相比，模拟结果表明，在各种情况下，我们提出的方法可以在估计准确性和鲁棒性方面获得更好的性能。
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.
