scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaojuan Xie ◽  
Shengliang Peng ◽  
Xi Yang

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.


Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 72-83
Author(s):  
Hl. S. Litvinovich ◽  
I. I. Bruchkouski

The researcher should choose the modes of recording spectra which allow to achieve the highest accuracy of spectral measurements in remote sensing systems. When registering a signal from aircraft which provide maximum coverage of the studied area, it is important to obtain a signal with the maximum signal-to- noise ratio in a minimum time, since the accumulation of spectra samples for averaging is impossible. The paper presents the experimental results of determining the noise components (readout noise, photon, electronic shot, pattern noise) for a monochrome uncooled CCD-line detector Toshiba TCD1304DG (CCD – charge-coupled devices) with various conditions of spectrum registration: detector temperature, exposition. Obtained dependences of the noise components make it possible to estimate the noise level for well-known conditions of spectra registration. The algorithm for processing CCD data based on an adaptive Wiener filter is proposed to increase the signal-to-noise ratio by using a priori information about the statistical parameters of the noise components. Such approach has allowed to increase the signal-to-noise ratio of sky spectral brightness by 4–9 dB for exposure times. The practical application of the algorithm has reduced the uncertainty in the vegetation index NDVI by 1.5 times when recording the reflection spectra of vegetation from the aircraft in the nadir measurement geometry.


2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.


2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2270 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Xueqiong Li

Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.


2021 ◽  
Author(s):  
Martijn van den Ende ◽  
Itzhak Lior ◽  
Jean Paul Ampuero ◽  
Anthony Sladen ◽  
Cédric Richard

<p>Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis as well as in monitoring of urban and marine environments, including microseismicity detection, ambient noise tomography, traffic density monitoring, and maritime vessel tracking. A major advantage of DAS is its ability to turn fibre-optic cables into large and dense seismic arrays. As a cornerstone of seismic array analysis, beamforming relies on the relative arrival times of coherent signals along the optical fibre array to estimate the direction-of-arrival of the signals, and can hence be used to locate earthquakes as well as moving acoustic sources (e.g. maritime vessels). Naturally, this technique can only be applied to signals that are sufficiently coherent in space and time, and so beamforming benefits from signal processing methods that enhance the signal-to-noise ratio of the spatio-temporally coherent signal components. DAS measurements often suffer from waveform incoherence, and processing submarine DAS data is particularly challenging.</p><p>In this work, we adopt a self-supervised deep learning algorithm to extract locally-coherent signal components. Owing to the similarity of coherent signals along a DAS system, one can predict the coherent part of the signal at a given channel based on the signals recorded at other channels, referred to as "J-invariance". Following the recent approach proposed by Batson & Royer (2019), we leverage the J-invariant property of earthquake signals recorded by a submarine fibre-optic cable. A U-net auto-encoder is trained to reconstruct the earthquake waveforms recorded at one channel based on the waveforms recorded at neighbouring channels. Repeating this procedure for every measurement location along the cable yields a J-invariant reconstruction of the dataset that maximises the local coherence of the data. When we apply standard beamforming techniques to the output of the deep learning model, we indeed obtain higher-fidelity estimates of the direction-of-arrival of the seismic waves, and spurious solutions resulting from a lack of waveform coherence and local seismic scattering are suppressed.</p><p>While the present application focuses on earthquake signals, the deep learning method is completely general, self-supervised, and directly applicable to other DAS-recorded signals. This approach facilitates the analysis of signals with low signal-to-noise ratio that are spatio-temporally coherent, and can work in tandem with existing time-series analysis techniques.</p><p>References:<br>Batson J., Royer L. (2019), "Noise2Self: Blind Denoising by Self-Supervision", Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California</p>


1983 ◽  
Vol 61 (2) ◽  
pp. 318-331 ◽  
Author(s):  
Denis Vincent ◽  
Gabriel Otis

We performed a theoretical and experimental study of a 10.6 μm heterodyne detection system with nonlinear postdetection. A single laser serves as both transmitter and local oscillator; the intermediate frequency is given by the Doppler effect due to a rotating target. An electrooptic crystal modulates the amplitude of the laser beam at a frequency of 15 kHz; a synchronous voltmeter measures the return signal after the nonlinear element. Values of the signal-to-noise ratio with respect to incident optical power agree with the results of the theoretical model. In particular, experimentally measured target-induced frequency spreading effects on the signal-to-noise ratio correspond to the predictions of the model. We also describe an experimental system.


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