This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE.

中文翻译：

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基于噪声学习的去噪自编码器

这封信介绍了一种新的降噪器，它修改了降噪自编码器 (DAE) 的结构，即基于噪声学习的 DAE (nlDAE)。建议的 nlDAE 学习输入数据的噪声。然后，通过从噪声输入中减去再生噪声来执行去噪。因此，当噪声比原始数据更容易再生时，nlDAE 比 DAE 更有效。为了验证 nlDAE 的性能，我们提供了三个案例研究：信号恢复、符号解调和精确定位。数值结果表明，与 DAE 相比，nlDAE 需要更小的潜在空间维度和更小的训练数据集。