Signal-to-noise Ratio Estimation Research Articles

3-D Tomographic Reconstruction of Rain Field Using Microwave Signals From LEO Satellites: Principle and Simulation Results

In this paper, we propose a novel approach for 3-D rain field reconstruction using satellite signals. It uses the estimated signal-to-noise ratio (SNR) at the ground receivers for low-earth orbit (LEO) satellites and, thus, indirectly estimates the path-integrated rain attenuation of the microwave communication links. A least-squares algorithm is employed to perform the 3-D tomographic reconstruction of the rain field. The proposed system model consists of an LEO satellite with a realistic overpass trajectory and multiple ground receivers with SNR estimators. Two synthetic rain events near the Great Barrier Reef in Australia are used to test the reconstruction outcome. Simulation results suggest that the reconstructed rain field has close agreement with the synthetic rain field.

WADA-W: A Modified WADA SNR Estimator for Audio-Visual Speech Recognition

One of the main challenges in speech recognition is developing systems that are robust to contamination by intrusive background noise. In audio-visual speech recognition (AVSR), audio information is augmented by visual information in order to help improve the performance of speech recognition, particularly when the audio modality is so significantly corrupted by background noise and it becomes hard to differentiate the original speech signal from the noise. The signal-to-noise ratio (SNR) can be used to identify the level of noise in original speech signal and one widely used method for SNR estimation is waveform amplitude distribution analysis (WADA), which is based on the assumption that the speech and noise signals have Gamma and Gaussian amplitude distributions respectively. Based on previous approaches, this work uses a precomputed look-up table as a reference for SNR estimation. In this study, WADA-white (WADA-W) has been developed, which rebuilds the precomputed look-up table using a white noise profile in combination of our own AVSR database. This new data corpus, namely the Loughborough University Audio-Visual (LUNA-V) dataset that contains recordings of 10 speakers with five sets of samples uttered by each speaker is used for this experimental work. We evaluate the performance of WADA-W on this database when it is corrupted by noise generated from three profiles obtained from the NOISEX-92 database included at varying SNR values. Evaluation of performance using the LUNA-V database shows that WADA-W performs better than the original WADA in terms of SNR estimation.

ALTIS: A new algorithm for adaptive long-term SNR estimation in multi-talker babble

We introduce a real-time capable algorithm which estimates the long-term signal to noise ratio (SNR) of the speech in multi-talker babble noise. In real-time applications, long-term SNR is calculated over a sufficiently long moving frame of the noisy speech ending at the current time. The algorithm performs the real-time long-term SNR estimation by averaging “speech-likeness” values of multiple consecutive short-frames of the noisy speech which collectively form a long-frame with an adaptive length. The algorithm is calibrated to be insensitive to short-term fluctuations and transient changes in speech or noise level. However, it quickly responds to non-transient changes in long-term SNR by adjusting the duration of the long-frame on which the long-term SNR is measured. This ability is obtained by employing an event detector and adaptive frame duration. The event detector identifies non-transient changes of the long-term SNR and optimizes the duration of the long-frame accordingly. The algorithm was trained and tested for randomly generated speech samples corrupted with multi-talker babble. In addition to its ability to provide an adaptive long-term SNR estimation in a dynamic noisy situation, the evaluation results show that the algorithm outperforms the existing overall SNR estimation methods in multi-talker babble over a wide range of number of talkers and SNRs. The relatively low computational cost and the ability to update the estimated long-term SNR several times per second make this algorithm capable of operating in real-time speech processing applications.

Extending a Nonlinear SNR Estimator to Include Shaping Distribution Identification for Probabilistically Shaped 64-QAM Signals

An estimation method for the nonlinear signal-to-noise ratio (SNR) is extended to simultaneously identify the shaping distribution for rate-adaptive probabilistically shaped (PS) 64-ary quadrature amplitude modulation (QAM) constellations. An artificial neural network (ANN) is trained with the fiber nonlinearity induced amplitude noise covariance and phase noise correlation extracted from received symbols. The identification employs estimates of the nonlinear signal-to-noise ratio $(\text{SNR}_{nl})$ and nonlinear system coefficients obtained from the ANN to extract the standardized constellation moments and hence identify the shaping distribution. The data used for training are 504 input–output sets, which are obtained from a simplified MATLAB simulation for a 32 Gbaud dual polarization PS 64-QAM signal considering seven different shaping distributions and a wide range of link configurations with varying fiber lengths and number of wavelength division multiplexed channels. Validation using 180 input–output sets that include five shaping distributions corresponding to a bit rate granularity of 25 Gb/s for bit rates between 200 and 300 Gb/s, exhibits high identification success rates, albeit for a limited set of examples. Reduced success rates are achieved for five shaping distributions corresponding to a bit rate granularity of 12.5 Gb/s for bit rates between 200 and 250 Gb/s. For completeness, the identification success rate is also quantified for the case of all seven shaping distributions in the validation.

Signal-to-noise ratio estimation for M-QAM signals in \u03b7\u2212\u03bcand \u03ba\u2212\u03bcfading channels

In this paper, signal-to-noise ratio (SNR) estimation is carried out by the method of moments (MOM) for fading channels modeled by probability distributions η−μ and κ−μ, considering M-ary quadrature amplitude modulation (M-QAM) with constellation energy normalized to one. New expressions are presented for the SNR estimation and for the mean, variance, and normalized mean square error (NMSE) of the estimates, obtained by a statistical linearization argument. Additionally, it is shown how to obtain the SNR estimate for Nakagami-m channel from the estimation derived for the models η−μ and κ−μ. The results obtained from the analytical expressions are corroborated by simulation results and show that the MOM is a suitable alternative for scenarios in which the mathematical tractability does not suggest the application of other estimation techniques.

A method for conversational signal-to-noise ratio estimation in real-world sound scenarios

The analysis of conversational signal-to-noise ratios (SNRs) measured in real-world scenarios can provide vital insight into people's communicative strategies and difficulties and guide development of hearing devices. However, measuring SNRs accurately and realistically is challenging in typical recording conditions, where only a mixture of sound sources is captured. This study introduces a novel method for realistic in situ SNR estimation, where the speech signal of a person in natural conversation is captured by a cheek-mounted microphone, adjusted for free-field conditions, and convolved with a measured impulse response to estimate the clean speech component at the receiver. A microphone near the receiver computes the noise-only component by applying a voice activity detector. Obtained SNR values are analyzed using in situ recordings of a real-world workspace meeting. It is shown that the temporal resolution is increased, and fluctuations in the speech level are more accurately tracked compared to a typical spectral-subtraction-derived method. The application of the proposed SNR estimation method may be valuable for compensation procedures in hearing instruments that take conversational dynamics into account.

Determine the significant digit of spectral data and reduce its redundant digits to eliminate the chance correlation problem based on the “salami slicing” method

In recent years, complex solution composition analysis based on spectroscopy has been a research hotspot for researchers and has broad application prospects. Improving the ability of complex solutions component analysis based on spectroscopy, eliminating spectral data redundancy and the chance correlation problem it brings has become an urgent issue. In order to solve these problems, this paper takes the dynamic spectrum (DS) as the research object, and uses the “salami slicing” method to process the DS data. Firstly, the significant digit of the decimal DS data is processed, and then the partial least-squares (PLS) method is applied to model and analyze the processed DS data. The turning point of the modeling accuracy's change is found, and the significant digit number of DS data is determined roughly. On this basis, the weight of the binary number is used skillfully to process the significant digit of DS. The processed DS data is modeled, and the significant digit number of the DS data is accurately analyzed to establish the efficacy of the proposed method. This method does not only improve the signal-to-noise ratio (SNR) of DS data, but also avoids the chance correlation problem due to the data redundancy. This method also provides a good means for SNR estimation of other spectral data and avoiding the chance correlation problem in modeling, and has a high application value.

FPGA-SDR Integration and Experimental Validation of a Joint DA ML SNR and Doppler Spread Estimator for 5G Cognitive Transceivers

In a multi-connected, multi-technology, and pervasive mobile infrastructure, such as what is being planned for 5G, artificial intelligence and cognition will play a major role. An important goal of future mobile infrastructures is to self-adapt their characteristics to their operating conditions, at the physical link, as well as at the network and application layers, which gives rise to a new paradigm known as context-aware cognitive radio (CR). CR transceivers (CTRs) mostly incorporate a cognitive engine that relies on various sensorial entities, which attempt to provide sufficient information about the quality of the link through the estimation of various key channel parameters. Two important parameters are required in a wide range of CTR architectures: the signal-to-noise ratio (SNR) and the Doppler spread. Within this context, we tackle the hardware design and integration of a joint data-aided (DA) maximum likelihood (ML) SNR and Doppler spread estimator recently shown to outperform main state-of-the-art solutions both in terms of accuracy and complexity. We propose a deep-pipelined and resource-efficient architecture for the outlined joint DA ML estimator, and we integrate our design on an FPGA-based software-defined radio (SDR) platform. We finally validate and test this prototype in real time under realistic over-the-air propagation conditions reproduced by a highly-scalabile channel emulator. Compared to its MATLAB floating-point version, our hardware prototype suggests negligible losses in performance despite the existence of several hardware impairments, thereby confirming its very strong potential and attractiveness for possible integration in future 5G CTRs.

Quantification of Signal-to-Noise Ratio in Cerebral Cortex Recordings Using Flexible MEAs With Co-localized Platinum Black, Carbon Nanotubes, and Gold Electrodes.

Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5–1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (<1500 Hz).

Subspace Averaging of Auditory Evoked Potentials.

The auditory evoked potential (AEP) is an electric potential generated in the brain in response to auditory stimuli. It has clinical importance in the detection of newborn infant hearing loss, among other applications. The signal-to-noise ratio (SNR) of the AEP is low, so signal averaging is typically employed to estimate it. Often, thousands of trials must be averaged before a sufficiently high SNR estimate is obtained. In this research, we have developed an AEP averaging method called subspace averaging. The subspace averaging method projects onto the signal subspace: the span of the principal eigenvectors of the signal autocorrelation matrix. The signal subspace has low dimensionality and captures the key features of the signal. Also, we introduce a new SNR estimator for AEP trials. Using our estimator, we compare SNR estimates of conventional averaging and subspace averaging. The subspace average has higher SNR compared to the conventional average.

SNR analysis and estimation for efficient phase noise mitigation in millimetre‐wave SC‐FDE systems

This study demonstrates a signal-to-noise ratio (SNR) analysis and estimation algorithm for efficient phase noise mitigation that can be practically applied to single-carrier frequency-domain-equalisation (SC-FDE) systems that operate in millimetre-wave bands. First, the effect of phase noise in SC-FDE systems is investigated on each of the packet reception processes, namely, channel estimation, SNR estimation, and data-field reception. According to the analysis, an SNR estimation algorithm is proposed. The performance of minimum-mean-square-error equalisation and conventional phase noise mitigation algorithm can be enhanced using the proposed SNR estimation. The effectiveness of the proposed analysis and SNR estimation algorithm is verified through the link-level simulation. Compared with the conventional SNR estimation and the iterative phase noise mitigation algorithms, the proposed algorithm provides a lower packet-error rate without any iterative decoding process.

Passive stochastic matched filter for Antarctic blue whale call detection.

As a first step to Antarctic blue whale (ABW) monitoring using passive acoustics, a method based on the stochastic matched filter (SMF) is proposed. Derived from the matched filter (MF), this filter-based denoising method enhances stochastic signals embedded in an additive colored noise by maximizing its output signal to noise ratio (SNR). These assumptions are well adapted to the passive detection of ABW calls where emitted signals are modified by the unknown impulse response of the propagation channel. A filter bank is computed and stored offline based on a priori knowledge of the signal second order statistics and simulated colored sea-noise. Then, the detection relies on online background noise and SNR estimation, realized using time-frequency analysis. The SMF output is cross-correlated with the signal's reference (SMF + MF). Its performances are assessed on an ccean bottom seismometer-recorded ground truth dataset of 845 ABW calls, where the location of the whale is known. This dataset provides great SNR variations in diverse soundscapes. The SMF + MF performances are compared to the commonly used MF and to the Z-detector (a sub-space detector for ABW calls). Mostly, the benefits of the use of the SMF + MF are revealed on low signal to noise observations: in comparison to the MF with identical detection threshold, the false alarm rate drastically decreases while the detection rate stays high. Compared to the Z-detector, it allows the extension of the detection range of 30 km in presence of ship noise with equivalent false discovery rate.

Electric field encephalography for brain activity monitoring

Objective. We describe an early-stage prototype of a new wireless electrophysiological sensor system, called NeuroDot, which can measure neuroelectric potentials and fields at the scalp in a new modality called Electric Field Encephalography (EFEG). We aim to establish the physical validity of the EFEG modality, and examine some of its properties and relative merits compared to EEG. Approach. We designed a wireless neuroelectric measurement device based on the Texas Instrument ADS1299 Analog Front End platform and a sensor montage, using custom electrodes, to simultaneously measure EFEG and spatially averaged EEG over a localized patch of the scalp (2 cm × 2 cm). The signal properties of each modality were compared across tests of noise floor, Berger effect, steady-state visually evoked potential (ssVEP), signal-to-noise ratio (SNR), and others. In order to compare EFEG to EEG modalities in the frequency domain, we use a novel technique to compute spectral power densities and derive narrow-band SNR estimates for ssVEP signals. A simple binary choice brain–computer-interface (BCI) concept based on ssVEP is evaluated. Also, we present examples of high quality recording of transient Visually Evoked Potentials and Fields (tVEPF) that could be used for neurological studies. Main results. We demonstrate the capability of the NeuroDot system to record high quality EEG signals comparable to some recent clinical and research grade systems on the market. We show that the locally-referenced EFEG metric is resistant to certain types of movement artifacts. In some ssVEP based measurements, the EFEG modality shows promising results, demonstrating superior signal to noise ratios than the same recording processed as an analogous EEG signal. We show that by using EFEG based ssVEP SNR estimates to perform a binary classification in a model BCI, the optimal information transfer rate (ITR) can be raised from 15 to 30 bits per minute—though these preliminary results are likely sensitive to inter-subject variations and choice of scalp locations, so require further investigation. Significance. Enhancement of ssVEP SNR using EFEG has the potential to improve visually based BCIs and diagnostic paradigms. The time domain analysis of tVEPF signals shows robust features in the electric field components that might have clinical relevance beyond classical VEP approaches.

Pilot-Based Time Domain SNR Estimation for Broadcasting OFDM Systems

The estimation of signal-to-noise ratio (SNR) is a major issue in wireless orthogonal frequency-division multiplexing (OFDM) system. In OFDM system, each frame starts with a preamble symbol that facilitates the SNR estimation. However, the performance of preamble-based SNR estimation schemes worsens in the fast-changing environment where channel changes symbol to symbol. Accordingly, in this paper, we propose a novel pilot-based SNR estimation scheme that optimally exploits the pilot subcarriers that are inserted in each data symbol of the OFDM frame. The proposed scheme computes the circular correlation between the received signal and the comb-type pilot sequence to obtain the SNR. The simulation results are compared with the conventional preamble-based Zadoff-Chu sequence SNR estimator. The results indicate that the proposed scheme generates near-ideal accuracy; especially in low SNR regimes, in terms of the normalized mean square error (NMSE). Moreover, this scheme offers a significant saving of computation over a conventional time domain SNR estimator.

New closed-form expressions for SNR estimates of Nakagami fading channels by the method of moments

Method of moments has been a parameter estimation technique appropriate to calculate signal-to-noise ratio (SNR) estimates in fading channel models in which an optimal technique like maximum likelihood estimation is not mathematically tractable. In this article, the ratio of the second moment squared to the fourth moment of the received signal envelope is considered to calculate an exact expression for the SNR estimate in Nakagami-m fading channel for M-QAM and $$\theta $$ -MQAM modulations as well as expressions to evaluate the variance and the mean of the estimate. The paper presents two useful contributions for SNR estimation theory on Nakagami fading. Besides the exact algebraic expression for the estimate for a generalized QAM modulation scheme, its performance is evaluated through a statistical linearization argument.

Learning Process for Reducing Uncertainties on Network Parameters and Design Margins

In this paper, we propose to lower the network design margins by improving the estimation of the signal-tonoise ratio (SNR) given by a quality of transmission (QoT) estimator, for new optical demands in a brownfield phase, based on a mathematical model of the physics of propagation. During the greenfield phase and the network operation, we collect and correlate information on the QoT input parameters, issued from the established initial demands and available almost for free from the network elements: amplifiers output power and the SNR at the coherent receiver side. Since we have some uncertainties on these input parameters of the QoT model, we use a machine learning algorithm to reduce them, improving the accuracy of the SNR estimation. With this learning process and for a European backbone network (28 nodes, 41 links), we could reduce the QoT inaccuracy by several dBs for new demands whatever the amount of uncertainties of the initial parameters.

Blind Estimation of Channel Order and SNR for OFDM Systems

In this paper, blind channel order and signal to noise ratio (SNR) estimators based on time-varying autocorrelation function (TVAF) are proposed for orthogonal frequency division multiplexing systems. The TVAF of the received signal reveals clearly the inherent periodicity introduced by cyclic prefix (CP) and carries rich information of channel and additive noise. To investigate the relationships among the TVAF, channel order, and SNR, a newly close-form expression of the TVAF of the received signal is derived. Not only the CP-induced TVAF components but the channel-spread TVAF elements are employed to derive the proposed estimators. Specifically, the two kinds of the TVAF components are separate from the components superimposed by the additive noise, which enables optimal estimation performance even for the low SNR regime. Numerical results show that, in contrast with the alternative methods, the proposed estimators achieve significant performance gain in the low SNR regime, while hold competitive performance in the high SNR regime.

Research of Snr Estimation and Prediction Method Used in Cognitive Radio

Cognitive radio is widely researched and concerned because of its cognitive and reconfigurable features. Cognitive radio can judge and evaluate channel quality, and adjust communication parameters in time to adapt to channel changes. One of the key factors to evaluate the channel status is the signal-to-noise ratio (SNR).This paper introduces a SNR estimation algorithm (M2M4 method), on the basis of Kalman filter theory, the SNR prediction algorithm is induced. The simulation results show that in the range of SNR (0 ~ 15dB), the estimation and prediction method exhibited low error rates, and the channel quality can be assessed in real time, so that the method can be used to estimate the channel quality of cognitive radio communication system.

Simultaneous Determination of Modulation Types and Signal-to-Noise Ratios Using Feature-Based Approach

This paper presents a low-complexity technique for simultaneous determination of modulation types and signal-to-noise ratios (SNRs) in wireless communication systems. The proposed approach exploits the extracted features of patterns observed in signals’ asynchronous amplitudes histograms, for the simultaneous determination of these quantities using support vector machine. Features extraction has been performed by a well-known technique called principal component analysis which is used to extract the most significant features before being supplied to the artificial intelligent system. Simulations for three commonly-used modulation types have been conducted under real-world channel conditions. The results conclude that the presented approach can accurately identify the modulation types with 99.83% accuracy despite the existence of real-world channel impairments. Furthermore, the algorithm is capable of SNRs estimation over a broad range of 0–30 dB with average estimation error of 0.79 dB. The proposed paper exploits the simplicity of generating asynchronous amplitudes histograms to enable cost-effective and reduced-complexity implementation in cognitive wireless systems.

High-Rate STBC Communication System Using Imperfect CSI Under Time-Selective Flat-Fading Environment

In this paper, a high-rate space-time block-coded (STBC) mobile communication system using imperfect channel-state-information (CSI) is presented, which is working over the time-selective flat-fading wireless channels. In the work, a flat-fading channel is modelled as a first-order Markov-process, taking into account Doppler shifts and carrier frequency offsets. The STBC data-rate 5/4 with two-transmitter antennas is attained by maximizing the coding gain and by minimizing the transmitted signal peak-to-minimum-power-ratio, while using the selective power scaling in combination with quadrature-phase-shift-keying digital modulation technique. The time-selective nature of underlying channel leads to reduction in the diversity gain, though optimum power scaling factor is incorporated. However, imperfect CSI usually arises due to the availability of noisy channel estimates at the receiver, which causes system performance degradation. Simulation results are presented to illustrate that the effective throughput in terms of frame-error-rate or symbol-error-rate of the presented data-rate $${5 \mathord{\left/ {\vphantom {5 4}} \right. \kern-0pt} 4}$$ STBC system is adversely affected because of the noisy channel estimates. But, an efficient channel estimator helps in combating the time-selectivity of channel, and in turn results in effective throughput improvement under the high signal-to-noise-ratio (SNR) conditions only. Subsequently, the data-rate $${9 \mathord{\left/ {\vphantom {9 8}} \right. \kern-0pt} 8}$$ STBC system with four-transmitter antennas is explored using similar strategy working over the time-nonselective (time-varying) flat-fading channels, which performs well under the high SNR and low channel estimation noise/error conditions.