SNR Estimation Research Articles

An improved Graph-based SNR Estimation Algorithm

An improved Graph-based SNR Estimation Algorithm

A Novel Noise Variance and SNR Estimator for OFDM-IM Systems over Nakagami-m Fading Channel

A Novel Noise Variance and SNR Estimator for OFDM-IM Systems over Nakagami-m Fading Channel

Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNNSup), physics-informed unsupervised DNN (DNNUns), and the conventional lookup table method (LUT) using simulation and in vivo data. MP-ASL was performed twice during resting state and once during the breath-holding task. First, the accuracy and noise immunity were evaluated in the first resting state. Second, CBF and CBVa values were statistically compared between the first resting state and the breath-holding task using the Wilcoxon signed-rank test and Cliff's delta. Finally, reproducibility of the two resting states was assessed. Simulation and first resting-state analyses demonstrated that DNNSup had higher accuracy, noise immunity, and a six-fold faster computation time than LUT. Furthermore, all methods detected task-induced CBF and CBVa elevations, with the effect size being larger with the DNNSup (CBF, p = 0.055, Δ = 0.286; CBVa, p = 0.008, Δ = 0.964) and DNNUns (CBF, p = 0.039, Δ = 0.286; CBVa, p = 0.008, Δ = 1.000) than that with LUT (CBF, p = 0.109, Δ = 0.214; CBVa, p = 0.008, Δ = 0.929). Moreover, all the methods exhibited comparable and satisfactory reproducibility. DNNSup outperforms DNNUns and LUT with respect to estimation performance and computation time.

SNR Estimation for 5G Satellite-to-Ground Communication in Ultra-Low SNR Environment Based on Channel Frequency Response Reconstruction

SNR Estimation for 5G Satellite-to-Ground Communication in Ultra-Low SNR Environment Based on Channel Frequency Response Reconstruction

A DoLP image enhancement method based on DGIF and multi-channel difference SNR estimation

A DoLP image enhancement method based on DGIF and multi-channel difference SNR estimation

Joint SNR and Rician K-Factor Estimation Using Multimodal Network Over Mobile Fading Channels

Joint SNR and Rician K-Factor Estimation Using Multimodal Network Over Mobile Fading Channels

SNR Estimation Arithmetic Base on 1 Bit Quantization Correlator (April 2024)

SNR Estimation Arithmetic Base on 1 Bit Quantization Correlator (April 2024)

Reasons for ceiling ratings in real-life evaluations of hearing aids: the relationship between SNR and hearing aid ratings.

In past Ecological Momentary Assessment (EMA) studies, hearing aid outcome ratings have often been close to ceiling. To analyze the underlying reasons for the very positive ratings, we conducted a study with 17 experienced hearing aid wearers who were fitted with study hearing aids. The acceptable noise level and the noise level where participants were unable to follow speech were measured. The participants then rated hearing aid satisfaction, speech understanding and listening effort for pre-defined SNRs between -10 and +20 dB SPL in the laboratory. These ratings were compared to ratings of a two-week EMA trial. Additionally, estimates of SNRs were collected from hearing aids during the EMA trial and we assessed whether the participants experienced those SNRs rated poorly in the laboratory in real life. The results showed that for hearing aid satisfaction and speech understanding, the full rating scale was used in the laboratory, while the ratings in real life were strongly skewed towards the positive end of the scale. In the laboratory, SNRs where participants indicated they could not follow the narrator ("unable to follow" noise level) were rated clearly better than the lowest possible ratings. This indicates that very negative ratings may not be applicable in real-life testing. The lower part of the distribution of real-life SNR estimates was related to participants' individual "unable to follow" noise levels and the SNRs which were rated poorly in the laboratory made up less than 10% of the speech situations experienced in real life. This indicates that people do not seem to frequently experience listening situations at SNRs where they are dissatisfied with their hearing aids and this could be the reason for the overly positive hearing aid outcome ratings in EMA studies. It remains unclear to what extent the scarcity of such situations is due lack of encounters or intentional avoidance.

Cyclic Correlation‐Based Blind SNR Estimation for Cyclic Prefix‐Less OFDM System

AbstractThis paper tackles the problem of the signal‐to‐noise ratio estimation for cyclic prefix (CP)‐Less orthogonal frequency division multiplexing (OFDM) system in the presence of Gaussian noise over multipath fading channels. First, we introduce the cyclic correlation of the CP‐Less OFDM signal with CP, and analyze the cyclic correlations for Gaussian noise and received signal. Second, we isolate the Gaussian noise power from the signal power. Finally, we obtain the close form expression of SNR estimation by using cyclic correlations. Computer simulation results verify the effectiveness of the proposed method.

Adaptive Coded Modulation for OFDM System

This paper studies the adaptive coded modulation for coded OFDM system using punctured convolutional code, channel estimation, equalization and SNR estimation. The channel estimation based on block type pilot arrangement is performed by sending pilots at every sub carrier and using this estimation for a specific number of following symbols. Signal to noise ratio is estimated at receiver and then transmitted to the transmitter through feedback channel ,the transmitter according to the estimated SNR select appropriate modulation scheme and coding rate which maintain constant bit error ratelower than the requested BER. Simulation results show that better performance is confirmed for target bit error rate (BER) of (10-3) as compared to conventional modulation schemes, the convolutional coded modulation offers a SNR gains of 5 dB compared to uncoded state at BER of 10-3. The proposed adaptive OFDM scheme maintains fixed BER under changing channel conditions.

An Analysis of Traditional Noise Power Spectral Density Estimators Based on the Gaussian Stochastic Volatility Model

Many single- and multi-channel speech enhancement techniques, old and new, rely in one way or another on estimates of the noise power spectral density (PSD). For example, the classical Wiener filter requires that either the speech or noise PSD be estimated. Typically, the noise PSD is estimated, as it is often easier to model and estimate than the speech. As a result, much attention has been paid to this important problem over the past couple of decades, with important scientific milestones being the minimum statistics (MS), the minima controlled recursive averaging (IMCRA), and the minimum mean squared (MMSE) estimators. Despite leading to major progress, these estimators are rather ad hoc, making them difficult to tune and improve in a systematic manner. In this article, we analyse some of the common heuristics employed in such noise PSD estimators to put them on firmer mathematical ground. More specifically, we use the Gaussian stochastic volatility model and show that the MMSE noise PSD estimator can be interpreted as a special case thereof. Moreover, we analyze the related problem of speech presence probability (SPP) estimation and show that the SPP estimation performed in the MMSE noise PSD estimator can be interpreted as an SNR estimator in the context of the Gaussian stochastic volatility model.

Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.

Nonlinear SNR estimation based on the data augmentation-assisted DNN with a small-scale dataset.

Fiber nonlinearity is one of the major impairments for long-haul transmission systems. Therefore, estimating the nonlinear signal-to-noise ratio (SNRNL) is indispensable to guarantee the quality of transmission and manage the operation of optical networks. The deep neural network (DNN) has been successfully applied for the SNRNL estimation. However, the performance substantially degrades, when the mega dataset is inaccessible. Here, we demonstrate an accurate SNRNL estimation based on the data augmentation (DA)-assisted DNN with a small-scale dataset in the frequency domain. When the 95-GBaud dual-polarization 16 quadrature amplitude modulation (DP-16QAM) signal with variable optical launch powers from -2 to 4-dBm is numerically transmitted from 80-km to 1520-km standard single-mode fiber (SSMF), we identify that, in comparison with traditional DNN scheme, the DA allows the reduction of the training dataset size by about 60% while keeping the same mean absolute error (MAE) as 0.2-dB of SNRNL estimation. Meanwhile, the DA-assisted DNN scheme can reduce the MAE by about 0.14-dB compared with the traditional DNN scheme, when both SNRNL estimation schemes use 100 raw datasets which contain 700 symbols. Due to these observations, the DA-assisted DNN scheme is promising for field-trial nonlinear SNR estimation, especially when the collection of mega datasets is inconvenient.

Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water quality parameters from Landsat-9 imagery based on both physics-based and machine learning modeling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Landsat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality products derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2 = 0.71). The improved constituent retrieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this assumption.

Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement

This paper proposes a Deep Learning (DL) based Wiener filter estimator for speech enhancement in the framework of the classical spectral-domain speech estimator algorithm. According to the characteristics of the intermediate steps of the speech enhancement algorithm, i.e., the SNR estimation and the gain function, there is determined the best usage of the network at learning a robust instance of the Wiener filter estimator. Experiments show that the use of data-driven learning of the SNR estimator provides robustness to the statistical-based speech estimator algorithm and achieves performance on the state-of-the-art. Several objective quality metrics show the performance of the speech enhancement and beyond them, there are examples of noisy vs. enhanced speech available for listening to demonstrate in practice the skills of the method in simulated and real audio.

MDG and SNR Estimation in SDM Transmission Based on Artificial Neural Networks

The increase in capacity provided by coupled SDM systems is fundamentally limited by MDG and ASE noise. Therefore, monitoring MDG and optical SNR is essential for accurate performance evaluation and troubleshooting. Recent works show that the conventional MDG estimation method based on the transfer matrix of MIMO equalizers optimizing the MMSE underestimates the actual value at low SNR. Besides, estimating the optical SNR itself is not a trivial task in SDM systems, as MDG strongly influences the electrical SNR after the equalizer. In a recent work we propose an MDG and SNR estimation method using ANN. The proposed ANN-based method processes features extracted at the receiver after DSP. In this paper, we discuss the ANN-based method in detail, and validate it in an experimental 73-km 3-mode transmission link with controlled MDG and SNR. After validation, we apply the method in a case study consisting of an experimental long-haul 6-mode link. The results show that the ANN estimates both MDG and SNR with high accuracy, outperforming conventional methods.

Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation

This paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adaptive manner. In order to estimate the SNR in the environment, not only is a convolutional neural network (CNN) applied in the proposed signal detection strategy, but a long shor-term memory network (LSTM) network is also included; in terms of features, it combines deep features and time-dimension features. The numbers of layers of the CNN and LSTM impact the performance of the algorithm. The decision on the presence or absence of a control signal is made at the fusion center (FC) based on the majority voting rule. This paper shows that the network with a two-layer CNN and a two-layer LSTM can achieve high estimation accuracy of environmental SNR. Simultaneously, the detection accuracy is improved by about 1 dB compared with the classical multichannel detection schemes.

Optimization of ACF-DSR-based joint Doppler shift and SNR estimator for Internet of Vehicle system

Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF-DSR (autocorrelation function-double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.

Deep learning speckle de-noising algorithms for coherent metrology: a review and a phase-shifted iterative scheme [Invited

We present a review of deep learning algorithms dedicated to the processing of speckle noise in coherent imaging. We focus on methods that specifically process de-noising of input images. Four main classes of applications are described in this review: optical coherence tomography, synthetic aperture radar imaging, digital holography amplitude imaging, and fringe pattern analysis. We then present deep learning approaches recently developed in our group that rely on the retraining of residual convolutional neural network structures to process decorrelation phase noise. The paper ends with the presentation of a new approach that uses an iterative scheme controlled by an input SNR estimator associated with a phase-shifting procedure.