LMS to Deep Learning: How DSP Analysis Adds Depth to Learning

Gorday, Paul; Erdöl, Nurgün; Zhuang, Hanqi

doi:10.1109/icassp.2019.8682669

Cited by 1 publication

(1 citation statement)

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“…Neural filtering was proposed to use a fully connected layer of neural network to approximate IIR/FIR filters with a nonlinear method [4] [5]. In [11], The author uses variable time delay neural network to demodulate pulse amplitude modulation(PAM) signal, and proved be able to learn feature detection equivalent to a matched filter or equalizing filter, depending on the modulation pulse shape. Different from the neural structures used in these works, herein we want to discover the capability of conventional neural layer other than feed-forward neural network.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Filtering for Intelligent Communications Signal Processing in Harsh Environments

Sun

Fan

2021

IEEE Access

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Aiming at utilizing artificial neural networks to enhance intelligent filtering for interfered wireless communication signal in harsh environments, a new method named convolutional neural filtering is designed and presented in this paper. This method is based on model-driven deep learning princeple, by analyzing the theoretical connection between the filter model and the convolutional neural layer, it attempts to use one-dimensional convolution kernels to learn a matched or bandpass filter. Moreover, the model introduces a kernel-wise attention mechanism between different convolution kernels to selectively emphasize informative filters. The results show that in terms of interference and noise suppression for received wireless signal, the filtering method has highlighted dynamic adaptability to variation of signals and interference, and it also reveals that the performance is affected by the initialization parameters and the number of convolution kernels. Based on this method an embeddable filtering unit fully based on neural network is provided, which can be easily integrated into a deep learning network targeting such as wireless signal detection and recognition applications, avoiding complex preprocessing for end-to-end wireless signal learning.

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Section: Related Workmentioning

confidence: 99%