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Periodic Signal Recovery and Detection from 1-Bit Measurements Using Convolutional Neural Network

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Abstract

In order to lower the hardware requirements of digital systems and to reduce the amount of collected data, 1-Bit sampling technique has received much attention. Following the trend, in this paper we try to address a classic problem based on the 1-Bit sampling data—the problem of detection and estimation of periodic signals in White Gaussian Noise. To achieve the tasks, the 1-D convolutional neural networks (CNN) are used to recover the waveforms of the periodic signals from the 1-Bit measurements. Subsequently, the method of generalized likelihood ratio test (GLRT) is applied on the recovered waveforms to detect the periodic signals and to estimate their unknown parameters. The simulation results show that CNN can recover the waveforms of periodic signals with a reasonable accuracy, and the parameters of frequency, time delay, initial phase, and relative amplitude can be obtained.

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Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research was funded by the Stable Supporting Fund of Acoustics Science and Technology Laboratory, the Youth Innovation Promotion Association CAS (No. 2021023) and the National Natural Science Foundation of China under Grants 61901273 and 51909254.

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Correspondence to Peng Xiao.

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Chen, Y., Xiao, P., Gao, Y. et al. Periodic Signal Recovery and Detection from 1-Bit Measurements Using Convolutional Neural Network. Circuits Syst Signal Process 43, 5328–5347 (2024). https://doi.org/10.1007/s00034-024-02717-y

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