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In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes' histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0-35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0-35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

This paper proposes a novel convolutional neural network (CNN) based joint classification method to characterize the signal-to-noise power ratio (SNR) and Doppler shift using spectrogram images, in order to enable efficient adaptive modulation and coding (AMC) designs. It is necessary to maintain high communication performances even in stringent environments where transceivers move at high speed due to the diversification of wireless applications. To optimize the transmission rate in such dynamic environments, AMC scheme is known to be effective. AMC is generally designed based on feedback information (FBI) such as the SNR and Doppler shift acquired on the receiving side. Here, the challenge is an increase in calculation burden, processing delay and estimation accuracy of the FBI. We focused on the spectrogram which is composed of power values in the time and frequency domains. Its two-dimensional fluctuation represents the Doppler shift as well as noise values. In the proposed method, such key information for AMC can be simultaneously extracted from a single spectrogram via a trained CNN. Therefore it is expected to contribute to reducing the computational burden and speeding up the signal processing. Simulation results are presented to demonstrate that the proposed method achieves better performance than traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Adaptive Modulation and Coding (AMC) scheme enabled by Artificial Neural Network (ANN) aided Signal-to-Noise power Ratio (SNR) estimation. The Power Spectral Density (PSD) values are trained for SNR classification and it is mapped to respective Modulation and Coding Scheme (MCS) sets. Once trained, optimal MCS can be determined in low calculation complexity. The proposed approach is robust especially in high mobility environment since the PSD appearance is hardly influenced by the Doppler shift. Its effectiveness in terms of throughput is presented through computer simulations compared to the existing Error Vector Magnitude (EVM) based link adaptation scheme.

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.

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.

Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. Methods: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. Results: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. Conclusion: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. Significance: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals. [ABSTRACT FROM AUTHOR]

Empirical-distribution-function (EDF)-based non-data-aided signal-to-noise ratio (SNR) estimation methods are effective for multilevel constellations but require many matching operations between reference cumulative distribution functions and EDF. To reduce resource consumption and improve real-time performance, we propose a low-complexity method by simplifying the matching test method. The proposed method modifies the matching operation from two-dimensions (2-D) to 1-D, reducing the number of matching operations and memory resource. Compared to the moment-based methods and the existing EDF-based reduced-complexity methods, the proposed method has the lowest complexity and provides better estimation performance in the medium-to-high SNR range. [ABSTRACT FROM AUTHOR]

Polar codes attract a lot of attention due to their provably capacity-achieving property with low encoding and decoding complexity. Frozen bits, the special structure of polar codes, are helpful for signal-to-noise ratio (SNR) estimation. A novel SNR estimation algorithm based on frozen bits is proposed in this letter. In the proposed algorithm, the mapping from SNR to frozen bit error rate is theoretically analyzed when code length is infinite and approximately calculated when code length is finite. Then, SNR can be estimated according to the mapping. Simulation results show that the proposed algorithm has better estimation performance with a relatively lower complexity. [ABSTRACT FROM PUBLISHER]

In this letter, we address the problem of closed-form data aided (DA) and non-data aided (NDA) maximum likelihood (ML) signal-to-noise ratio (SNR) estimation for hyper-cubic modulated signals over Gaussian channel. The requirement of high SNR is exploited to derive closed-form expression of unbiased NDA ML SNR estimator. Exact expressions for the DA and NDA Cramer-Rao lower bound (CRLB) on the variance of these estimators are also determined analytically. The Monte Carlo simulation method is used to compute the normalized mean squared error (NMSE) of the estimators, and the results show that the NMSEs approach the respective normalized CRLBs. [ABSTRACT FROM PUBLISHER]

In view of the issue that the single-signal signal-to-noise ratio (SNR) estimation methods are unsuitable for time-frequency overlapped signals in underlay cognitive radio (CR), a novel SNR estimation method for time-frequency overlapped signals is proposed under low SNR conditions. In this method, the SNR estimation is transformed into normalized power estimation of every signal component through establishing the kurtosis of received signals on the basis of second moments and forth-order moments. The Cramer Rao Bound (CRB) of SNR estimation for time-frequency overlapped signals is also derived in this letter. Simulation results are provided to confirm that the proposed method can estimate the SNR of time-frequency overlapped signals effectively in low SNR regions. [ABSTRACT FROM PUBLISHER]

Target detection is an important issue in the unattended ground sensors. In this paper, inspired by the idea of subspace-based direction of arrival estimation algorithms, a new target detection algorithm called subspace-based target detection (SBTD) is proposed to detect moving targets. The SBTD employs the SNR of the acoustic signals to decide whether moving targets are exiting or not. Although the SBTD has good detection performance, its cost maybe a little high for unattended sensors with low-cost hardware and long-term monitoring. To relieve the cost, we propose the hierarchical detection scheme and develop a two-stage detection method based on the SBTD for target detection in the wild, in which the first stage detection algorithm is chosen from current detection algorithms, while the second stage detection algorithm employs the SBTD. Experiments are conducted to verify the proposed detection method through acoustic signals gathered by the micro-electro-mechanical systems (MEMS) microphone array in the wild. Results show that the detector constructed by our two-stage detection method cannot only estimate the SNR of the acoustic signals but also can reduce the false alarm rate significantly with the detection rate almost unchanged in comparison with the detector chosen by its first-stage detection algorithm. The results indicate that a better detection performance is achieved in terms of the receiver operator characteristic curves. [ABSTRACT FROM PUBLISHER]

Two complexity reducing schemes are proposed in this letter for the recently presented Kolmogorov-Smirnov (K-S) test based signal-to-noise ratio (SNR) estimator. The K-S test based SNR estimator can work properly over an extended SNR range for various multilevel constellations with limited signal samples, but involves considerably more add operations as a result for the huge amount of reference signals needed for matching operations. The proposed two complexity reducing schemes explore the order characteristic of the SNR matching pool to accelerate the searching procedure. For the situation under consideration, the computational complexities (numbers of add operation) of the two proposed schemes are about 1/5 and 1/20 of the original one respectively. Simulation results have verified these schemes' effectiveness. [ABSTRACT FROM PUBLISHER]

A novel non-data-aided (NDA) signal-to-noise ratio (SNR) estimator for multilevel constellations based on Kolmogorov-Smirnov test is proposed in this paper. The empirical cumulative distribution function (ECDF) of certain decision statistic derived from the received signal is computed and compared with pre-stored cumulative distribution functions (CDFs) or ECDFs of reference signals with known SNRs. Then, the specific SNR, with which the pre-stored CDFs or ECDFs is the most closest to the ECDF of the received signal, is selected as the estimate. The characteristic of this estimator is to convert the estimation problem to a pattern recognition one. Extensive simulation results demonstrate that, compared with the traditional Method-of-Moment (MoM) based estimators, the proposed estimator can work properly over an extending SNR range for various multilevel constellations. With limited signal samples, the estimator offers superior estimation performance than the classic M2M4 estimator and newly presented M8 estimator. [ABSTRACT FROM PUBLISHER]

The problem of estimating the average signal-to-noise ratio (SNR) in a non-coherent M-ary frequency shift keying (NCMFSK) receiver has been considered, where the transmitted signal is propagated over a noisy Ricean fading channel. Closed-form maximum likelihood estimators for both the data-aided and non data-aided scenarios have been derived using the approximations of the modified Bessel functions. Numerical results are presented and the performance of the estimators is evaluated by comparing with a Cramer-Rao bound. [ABSTRACT FROM PUBLISHER]

A sensor network is used for distributed signal-to-noise ratio (SNR) estimation in a single-time snapshot. Sensors observe a signal embedded in noise, and each observation is phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the mean and variance are estimated jointly, using an asymptotically minimum-variance estimator. It is shown that this joint estimator decouples into simple individual estimators of the mean and the variance. The constant-modulus phase modulation scheme ensures a fixed transmit power, robust estimation across several sensing noise distributions, as well as an SNR estimate that requires a single set of transmissions from the sensors to the fusion center. The estimators are evaluated in terms of asymptotic variance, which are then used to evaluate the performance of the SNR estimator with Gaussian and Cauchy sensing noise distributions in the cases of total transmit power constraint as well as a per-sensor power constraint. For each sensing noise distribution, the optimal phase transmission parameters are also determined. The asymptotic relative efficiency of the estimators is evaluated. It is shown that among the noise distributions considered, the estimators are asymptotically efficient only when the noise distribution is Gaussian. Simulation results corroborate analytical results. [ABSTRACT FROM AUTHOR]

This correspondence deals with the problem of estimating average signal-to-noise ratio (SNR) for a communication link employing binary frequency shift keying (BFSK) in the presence of a carrier frequency offset (CFO). The transmitted symbols are corrupted by Rayleigh fading and additive white Gaussian noise (AWGN). We treat the CFO as a nuisance parameter and estimate it using a data statistics based estimator. This estimate is then used to design a maximum likelihood (ML) estimator to get the estimates of SNR. We also derive the Cramér–Rao bound (CRB) for the estimators and have shown the performance of both the data-aided and non-data-aided estimators. [ABSTRACT FROM PUBLISHER]

Signal-to-noise ratio (SNR) estimators of linear modulation schemes usually operate at one sample per symbol at the matched filter output. In this paper we propose a new method for estimating the SNR in the complex additive white Gaussian noise (AWGN) channel that operates directly on the oversampled cyclostationary signal at the matched filter input. Exploiting cyclostationarity proves to be advantageous due to the fact that a signal-free Euclidean noise subspace can be identified such that only second order moments of the received waveform need to be computed. The proposed method is nondata-aided (NDA), as well as constellation and phase independent, and only requires prior timing synchronization to fully exploit the cyclostationarity property. The estimator can also be applied to nonconstant modulus constellations without requiring any tuning, which is a feature not found in existing approaches. Implementation aspects and simpler suboptimal solutions are also provided. [ABSTRACT FROM PUBLISHER]

This paper addresses the data-aided (DA) signal-to-noise ratio (SNR) estimation for constant modulus modulations over time-variant flat Rayleigh fading channels. The time-variant fading channel is modeled by considering the Jakes' model and the first order autoregressive (AR1) model. Closed-form expressions of the Cramér–Rao bound (CRB) for DA SNR estimation are derived for known and unknown fast fading Rayleigh channels parameters cases. As special cases, the CRBs over slow and uncorrelated fading Rayleigh channels are derived. Analytical approximate expressions for the CRBs are derived for low and high SNR. These expressions that enable the derivation of a number of properties that describe the bound's dependence on key parameters such as SNR, channel correlation and sample number. Since the exact maximum likelihood (ML) estimator is computationally intensive in the case of fast-fading channels, two approximate ML estimator solutions are proposed for high and low SNR cases in the case of known channel parameters. The performances of theses estimators are examined analytically in terms of means and variances. In the presence of unknown channel parameters, a high SNR ML estimator based on the AR1 correlation model is derived. It is shown that the ML estimates of the SNR parameter and unknown channel parameters may be obtained in a separable form. Finally, simulation results illustrate the performance of the estimator and confirm the validity of the theoretical analysis. [ABSTRACT FROM PUBLISHER]

The ballistocardiogram (BCG) is a measure of the reaction force of the body to cardiac ejection of blood. A variety of systems can be used for BCG detection, including beds, tables, chairs, and weighing scales. Weighing scales, in particular, have several practical advantages over the alternatives: low cost, small size, unobtrusiveness, and familiarity to the user; one disadvantage is that the subject must stand during the recording, rather than sit or lay supine, resulting in a higher susceptibility to motion artifacts in the measured signal. This paper evaluates the electromyogram (EMG) signal acquired from the feet of the subject during BCG recording as a noise reference for standing BCG measurements. As a subject moves while standing on the scale, muscle contractions in the feet are detected by the EMG signal, and used to flag segments of the BCG signal that are corrupted by elevated noise. For the purposes of evaluating this method, estimates of the BCG noise-to-signal ratio (NSR) were independently calculated with an ensemble average method, using the R-wave of a simultaneously-acquired chest ECG as a timing reference. The linear correlation between EMG power alone and BCG NSR from 14 subjects was found to be moderate ( r = 0.58, F-statistic p -value < 0.05); combined with body-mass index (BMI), multiple linear regression yielded a stronger correlation (r = 0.73, F -statistic p-value = 0.01). Additionally, an example usage of the lower-leg EMG for improving BCG measurement robustness is provided. [ABSTRACT FROM PUBLISHER]

We propose a new SNR estimation method based on the preamble for OFDM systems in frequency selective channels. The OFDM training symbols in the preamble are equalized by the known data in frequency domain and employed to estimate the noise variance. The second order moments of the received symbols are used to estimate the signal plus noise power in the OFDM packets. The SNRs on the subchannels and the average SNR of the packets can all be estimated. Simulation results show that the proposed method is robust to frequency selectivity in wireless channels, and its performance is considerably improved compared with the available methods. [ABSTRACT FROM AUTHOR]

This paper presents analysis of performance measurements from a MIMO-OFDM IEEE 802.11n hardware implementation at 5.2 GHz using four transmitters and four receivers. Two spatial multiplexing systems are compared; one which uses a zero-forcing (ZF) detector and the other a list sphere detector (LSD). We show that the measured results do not align with standard prediction based on simulation assuming uncorrelated receiver noise. We show that the discrepancy can be explained by the inclusion of transmitter noise into the channel model. This effect is not included in existing MIMO-OFDM channel models. The measured results from our hardware implementation show successful packet transmission at 600 Mb/s with 15 bits/s/Hz spectral efficiency at 73% coverage for ZF and 84% coverage for LSD with an average receiver signal to noise ratio (SNR) of 26 dB. [ABSTRACT FROM AUTHOR]

Dynamic infrared imaging has been proposed in literature as an adjunctive technique to mammography in breast cancer diagnosis. It is based on the acquisition of hundreds of consecutive thermal images with a frame rate ranging from 50 to 200 frames/s, followed by the harmonic analysis of temperature time series at each image pixel. However, the temperature fluctuation due to blood perfusion, which is the signal of interest, is small compared to the signal fluctuation due to subject movements. Hence, before extracting the time series describing temperature fluctuations, it is fundamental to realign the thermal images to attenuate motion artifacts. In this paper, we describe a method for the quantitative evaluation of any kind of feature-based registration algorithm on thermal image sequences, provided that an estimation of local velocities of reference points on the skin is available. As an example of evaluation of a registration algorithm, we report the evaluation of the SNR improvement obtained by applying a nonrigid piecewise linear algorithm. [ABSTRACT FROM AUTHOR]

The problem of estimating the signal-to-noise ratio for time-hopping binary pulse position modulated signals, time- hopping binary phase shift keying signals, and direct-sequence binary phase shift keying signals in an ultra-wide bandwidth (UWB) system is studied. Both an additive white Gaussian noise channel and a multipath fading channel are considered. Several new estimators are derived by making use of different properties of the UWB signals. The performances of the estimators are examined and compared in terms of their root-mean-squared errors. Numerical results show that they have excellent performances when operating in an ultra-wide bandwidth system. [ABSTRACT FROM PUBLISHER]

In this paper we consider the problem of estimating the signal-to-noise ratio (SNR) of the received signal in a QPSK data transmission system. This estimation must be done in-service (non-data aided), without carrier phase knowledge and without symbol timing phase knowledge. New algorithms with these features are derived from the ratio of two moments. The performance of these algorithms is studied by means of simulations, and it is shown that they are robust against carrier and symbol timing frequency offsets as well. This makes them suitable for a wide range of applications. [ABSTRACT FROM PUBLISHER]

The successful DVB standard has now evolved into the DVB-S2 standard, which promises to bring very significant capacity gains. The main DVB-S2 feature is its adaptive air interface, where coding and modulation techniques are varied flexibly to maximize performance and coverage. This article addresses the design of the entire DVB-S2 communication chain, considering practical algorithms for coding, modulation, predistortion, carrier and SNR estimation, frame synchronization, and data recovery. The design complexity is exacerbated by the fact that DVB-S2 foresees 28 different coding/modulation pairs, demanding specific optimization and variable frame length. The performance achieved considering all possible impairments is compared to the ideal performance achievable in the Gaussian channel in terms of integral degradation, which ranges from 0.4 to 2,5 dB in going from QPSK to 32-APSK. [ABSTRACT FROM PUBLISHER]

A new method of estimating the signal-to-noise ratio for M-PSK receivers was presented previously, which derives that estimate from the locked-state value of a new family of carrier lock detectors. A quantitative analysis is undertaken in which it is shown that this method requires significantly less hardware and/or software resources than estimation from the received signal's error rate, is irrespective of the data sequence, and necessitates far fewer symbols in order to arrive at an equally accurate estimate. [ABSTRACT FROM PUBLISHER]

This letter deals with the problem of non data aided (NDA) signal to noise ratio (SNR) estimation of OFDM signals transmitted through unknown multipath fading channel. Most of existing OFDM SNR estimators are based on the knowledge of pilot sequences which is not applicable in some contexts such as cognitive radio for instance. We show that it is possible to take advantage of the periodic redundancy induced by the cyclic prefix to get an accurate NDA SNR estimator. Numerical simulations highlight the benefit of the proposed method compared with the state of the art. [ABSTRACT FROM PUBLISHER]

Signal-to-noise ratio (SNR) is an important parameter in many receivers. In this letter, we derive a maximum-likelihood estimate of the amplitude of a binary phase-shift keying modulated signal and develop an iterative SNR search algorithm. Low bias is achieved for low SNR. [ABSTRACT FROM PUBLISHER]

We derive Cramer-Rao lower bounds (CRLBs) for the estimation of signal-to-noise ratio (SNR) of binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) modulated signals. The received signal is corrupted by additive white Gaussian noise (AWGN). The lower bounds are derived for non-data-aided estimation where the transmitted symbols are unknown at the receiver. The bounds are compared to those for data-aided estimations (known symbols at the receiver). It is shown that at low SNR there is a significant difference between the bounds for non-data-aided and data-aided estimations [ABSTRACT FROM PUBLISHER]

Theoretically, it is necessary to estimate the SNR when using a MAP of the log-MAP constituent decoder. The effect of an SNR mismatch on the bit error rate performance of turbo-codes and the design of good variance estimators have been addressed by several authors. In this letter we study the SNR sensitivity of turbo-decoding with log-MAP and max-log-MAP constituent decoders respectively for AWGN and Rayleigh fading channels. Our theoretical and simulation results indicate that an estimation of the SNR is not necessary from a practical point of view. Our setup is aligned with decoder implementation aspects of future mobile communication systems [ABSTRACT FROM PUBLISHER]