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Robust adaptive beamforming (RAB) can be used to suppress interference signals while retaining the desired signals received by a sensor array. However, desired signal self-cancellation and model mismatch can affect RAB performance. In this paper, a novel interference-plus-noise covariance matrix (INCM) reconstruction method is proposed for RAB to solve these problems. The proposed method divides the desired signal into two ranges according to the input signal-to-noise ratio (SNR), namely low SNR and high SNR. In the low SNR range, INCM reconstruction directly uses the same sample covariance matrix as the sample matrix inversion (SMI) beamformer to retain the advantages of the traditional SMI algorithm. In the high SNR range, the eigenvalues of the sample covariance matrix are used to estimate the interference power and noise power. The optimized interference steering vector (SV) is obtained by solving a quadratic convex optimization problem in an interference subspace. The INCM is reconstructed from the interference SVs, interference power, and noise power. The reconstructed INCM is then used to correct the desired signal SV via maximizing the beamformer output power. This is achieved by solving a quadratically constrained quadratic programming (QCQP) problem. Analysis and simulation results are presented which demonstrate that the proposed method performs well under a variety of mismatch conditions. [ABSTRACT FROM AUTHOR]

This 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. Key Points: A cyclic prefix (CP)‐less orthogonal frequency division multiplexing system is presentedAlignment signals are designed to eliminate inter symbol interference caused insufficient CP lengthCyclic correlation of CP is used to estimate signal‐to‐noise ratio [ABSTRACT FROM AUTHOR]

Summary: In this paper, the channel estimation and signal‐to‐noise ratio (SNR) estimation technique of single‐carrier frequency domain equalization (SC‐FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE‐KF) are proposed. This paper first introduces the SC‐FDE system and introduces the principle of MEE‐KF, and then, the channel estimation flow based on MEE‐KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE‐KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC‐FDE system has also been greatly improved. [ABSTRACT FROM AUTHOR]

Powerful and reliable estimation of transmission parameters is an indispensable task in each receiver unit—not only for radio frequency, but also for optical wireless communication systems. In this context, the signal-to-noise ratio (SNR) plays an eminent role, especially for adaptive scenarios. Assuming a bandlimited optical intensity channel, which requires a unipolar waveform design, an algorithm for SNR estimation is developed in this paper, which requires no knowledge of the transmitted data. This non-data-aided approach benefits to a great extent from the fact that very long observation windows of payload symbols might be used for the estimation process to increase the accuracy of the result; this is in striking contrast to a data-aided approach based on pilot symbols reducing the spectral efficiency of a communication link. Since maximum likelihood, moment-based or decision-directed algorithms are not considered for complexity and performance reasons, an expectation-maximization solution is introduced whose error performance is close to the Cramer-Rao lower bound as the theoretical limit, which has been derived as well. [ABSTRACT FROM AUTHOR]

Not only for radio frequency but also for optical communication systems, knowledge of the signal-to-noise ratio (SNR) is essential, e.g., for an adaptive network, where modulation schemes and/or error correction methods should be selected according to the varying channel states. In the current paper, this topic is discussed for a bandlimited optical intensity link under the assumption that the data symbols are known to the receiver unit in form of pilot sequences. This requires a unipolar signal design regarding the symbol constellation, but also a non-negative pulse shape satisfying the Nyquist criterion is necessary. Focusing on this kind of scenario, the modified Cramer–Rao lower bound is derived, representing the theoretical limit of the error performance of the data-aided SNR estimator developed in this context. Furthermore, we derive and analyze a maximum likelihood algorithm for SNR estimation, which turns out to be particularly simple for specific values of the excess bandwidth, among them the most attractive case of minimum bandwidth occupation. Numerical results confirming the analytical work conclude the paper. [ABSTRACT FROM AUTHOR]

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In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model (CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably. [ABSTRACT FROM AUTHOR]

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]

A novel technique based on cubic spline interpolation with linear least square regression (CSILLSR) is developed to calculate the signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The SNR from CSILLSR method is compared with methods of nearest, linear interpolation, a combination of linear interpolation and nearest, nonlinear least square regression, autocorrelation Levinson- Durbin recursion, and adaptive slope nearest neighbourhood. Samples of SEM images with various accelerating voltages, beam diameters, surface tilts and contrast were applied to evaluate the performance of CSILLSR method in terms of SNR values of the SEM images. The new method is able to generate more accuracy results than the other six methods. In addition, the CSILLSR Wiener filter appears to be the best filter to reduce and remove white Gaussian noise from SEM images as compared to the average filter and median filter. [ABSTRACT FROM AUTHOR]

Adaptive coded and modulation (ACM) is an effective measure to resist rain attenuation in Ka-band satellite communications. The accuracy of the estimator for signal-to-noise ratio (SNR) is one of the main factors that affect ACM performance. This paper establishes the channel model of satellite communication in Ka-band and derives and analyzes data-aided (DA) maximum likelihood (ML) and method-of-moments estimators for SNR of continuous phase modulation (CPM) in Ka-band fading channel. Simulations and analysis indicate that the normalized mean square error (NMSE) of the DA ML estimator is closer to the Cramer-Rao bounds (CRB) at low SNR, but the performance worsens with the SNR increases from medium to high values. The M2M4 estimator performs poorly at low SNR and best at medium SNR. When the SNR is low, the performance of the DA ML estimator is better than the M2M4 estimator; however, the two estimators have similar performance when SNR is high. The performances of the two estimators will become increasingly better the greater the lengths of the observation signals become. However, the influence of the signal length will become increasingly smaller as the SNR becomes larger. [ABSTRACT FROM AUTHOR]

The law of energy distribution of white noise (WN) in Haar wavelet decomposition levels (WDLs) of cardiovascular bio-signals including Phonocardiogram (PCG), Electrocardiogram (ECG), and Sphygmogram (SPG) is analyzed statistically. The analytical results indicate that for WN signals, energy proportion of the 2nd WDL to the 1st WDL is 0.5 approximately, which is verified by Monte Carlo test. For PCG, ECG and SPG signals, when sampling frequency is 4 kHz for PCG, 400 Hz for ECG, and 160 Hz for SPG, energies of the 1st WDL and 2nd WDLs are almost the same; this is validated by theoretical analysis and practical observation on collected datasets. Based on the wavelet energy distribution laws of WN and cardiovascular bio-signals, equations are created to estimate energy of WN, energy of bio-signals, and SNR. Experimental results show that the proposed SNR estimation method for cardiovascular bio-signals mixed with WN has the normalized mean square error of less than 0.1 in all cases considered. [ABSTRACT FROM AUTHOR]

This paper proposes a method to select multi-antenna signals based on SNR estimation, in order to solve the negative influence of the poor partial quality branch signals in multi-antenna system. The algorithm proposes antenna selecting programs, based on the SNR estimation, the Cramer Rao bound and the combined signal matched filtering, to avoid the branch poor signals and enhance the signal combine quality. Theoretical and simulation results show that the proposed algorithm has a better performance than the traditional method, and the performance of the algorithm is improved with the increase of the number of antennas and the difference of the signal quality. [ABSTRACT FROM AUTHOR]

Signal-to-noise ratio (SNR) estimation available in the literature are designed based on the assumption of Gaussian noise models. These estimators may produce misleading results when the distribution of the noise deviates from Gaussian. This paper investigates the performance of existing SNR estimators in an additive non-Gaussian noise channel based on a Gaussian mixture model. An expectation-maximization (EM) based approach is proposed for optimum SNR estimation in the non-Gaussian noise channel. In addition, the Cramer-Rao bound is derived and used as a benchmark to assess the performance of the SNR estimators. Simulation results confirm the optimality and robustness of the proposed EM-based estimator in Gaussian and non-Gaussian noise channels. [ABSTRACT FROM AUTHOR]

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods. [ABSTRACT FROM AUTHOR]

An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Estimation of signal-to-noise ratio (SNR) is an important problem in wireless communication systems. It has been studied for various constellation types and channels using different estimation techniques. Maximum likelihood estimation is a technique which provides efficient and in most cases unbiased estimators. In this paper, we have applied maximum likelihood estimation for systems employing square or cross QAM signals which are undergoing slow flat Rayleigh fading. The problem has been considered under various scenarios like data-aided (DA), non-data-aided (NDA) and partially data-aided (PDA) and the performance of each type of estimator has been evaluated and compared. It has been observed that the performance of DA estimator is best due to usage of pilot symbols, with the drawback of greater bandwidth consumption. However, this can be catered for by using partially data-aided estimators whose performance is better than NDA systems with some extra bandwidth requirement. [ABSTRACT FROM AUTHOR]

We propose a novel technique for automatic classification of modulation formats/bit-rates of digitally modulated signals as well as non-data-aided (NDA) estimation of signal-to-noise ratio (SNR) in wireless networks. The proposed technique exploits modulation format, bit-rate, and SNR sensitive features of asynchronous delay-tap plots (ADTPs) for the joint estimation of these parameters. Simulation results validate successful classification of three commonly-used modulation formats at two different bit-rates with an overall accuracy of 99.12%. Similarly, in-service estimation of SNR in the range of 0−30 dB is demonstrated with mean estimation error of 0.88 dB. The proposed technique requires low-speed asynchronous sampling of signal envelope and hence, it can enable simple and cost-effective joint modulation format/bit-rate classification and NDA SNR estimation in future wireless networks. [ABSTRACT FROM AUTHOR]

The performance of the coded orthogonal modulation (OM) system under slow fading channels heavily depends on the estimation of the signal-to-noise ratio (SNR), including the fading amplitude and the noise spectral density. However, a relatively long packet of pilot symbols is often required to guarantee the accuracy of the SNR estimation, which makes it impractical in some situations. To address this problem, this paper proposes an iterative SNR estimation algorithm using the soft decoding information based on the expectation-maximization algorithm. In the proposed method, a joint iterative loop between the SNR estimator and decoder is performed, where the extrinsic information generated by the soft decoder is employed to enhance the estimation accuracy and the SNR estimated by the estimator is used to generate the soft information to the decoder. Also, no pilot symbols are needed to estimate the SNR in the proposed estimator. The Cramer-Rao lower bound (CRLB) of fully data-aided (FDA) estimation is derived to works as the final benchmark. The performance of the proposed algorithm is evaluated in terms of the normalized mean square errors (NMSEs) and the bit error rates (BERs) under block fading channels. Simulation results indicate that the NMSE of the proposed estimator reaches the CRLB of the FDA estimator and outperforms that of the approximate ML (ML-A) estimator proposed by Hassan et al. by 4.1 dB. The BER performance of coded OM system with the proposed estimation algorithm is close to the ideal case where the channel fading and the noise spectral density are known at the receiver. [ABSTRACT FROM AUTHOR]

White Gaussian noise (WGN) commonly exists in acquisition and transmission of heart sound (HS) signals. The energy distributions of WGN and HS in wavelet decomposition levels (WDLs) are explored. The statistical analysis indicates that for WGN, energy proportions of detail portions of WDLs and energy proportion of the 2nd WDL are fixed. This finding is verified by using Monte Carlo test. Moreover, for a HS signal recorded under sampling frequency of 4 kHz, energies of the 1st and 2nd WDL are almost the same, which are validated by theoretical analysis and practical observation on three HS benchmark datasets. Based on these findings, equations estimating WGN energy and signal to noise ratio (SNR) for a noisy HS signal are created. In addition, a novel energy distribution-based wavelet multi-threshold de-noising approach (ED-WMTD) is proposed to reduce WGN. In which, firstly based on the energy distribution in WDLs and estimated energy of WGN, WGN energy in detail portion of each WDL is figured out. Then, soft-threshold method is adopted. The best threshold in a WDL is defined as the one by which the energy loss of noisy HS signal in this WDL is most similar to the energy of WGN in detail portion of this WDL. The accuracy of such a WGN energy estimation method is evaluated by average error of SNR estimation. ED-WMTD is assessed using mean square error and compared with four generally used WMTD methods. Experimental results show that this novel HS de-noising approach not only filters out HS noise effectively but also well retains its pathological information. [ABSTRACT FROM AUTHOR]

Highlights: [•] Novel coherence-based noise reduction technique for binaural hearing aids. [•] Based on spatial properties of target and undesired interfering signals. [•] Binaural noise reduction algorithm derived using a Wiener filter. [•] Assessment using normal-hearing subjects in reverberant environments. [•] Significant improvements in terms of speech intelligibility over unprocessed signals. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low-power adaptive low-density parity check (LDPC) decoder that utilizes dynamic voltage and frequency scaling to reduce power consumption. Most existing adaptive LDPC decoders have focused only on the decoding performance based on the signal-to-noise ratio (SNR) estimation. However, significant idle power is consumed when the decoder awaits the next frame after processing a frame. In mobile communication standards such as China Mobile Multimedia Broadcasting and Digital Video Broadcasting Satellite Second Generation, adaptive coding and modulation has been adopted. Thus, it is possible to reduce the power consumption efficiently by using the SNR estimation. In this paper, we apply a customized frequency selection scheme and a variable voltage generation scheme to an adaptive LDPC decoder to reduce the dynamic power consumption. The proposed schemes result in a reduction of 44% in the energy consumption of an LDPC decoder implemented using 0.18-μm complementary metal-oxide-semiconductor technology. [ABSTRACT FROM AUTHOR]

In the channel-varying environment, it is very important to estimate the signal to noise ratio (SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying or quadrature amplitude modulation. In this paper, we propose a SNR estimation method which uses zero point auto-correlation of received signal per block and auto/cross-correlation of decision feedback signal in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the previous SNR estimation methods. Also, we mathematically derive the SNR estimation expression according to computational difference of auto/cross-correlation. Finally, Monte Carlo simulations are used to verify the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an a priori signal-to-noise ratio (SNR) estimator with a modified sigmoid gain function is proposed for real-time speech enhancement. The proposed sigmoid gain function has three parameters, which can be optimized such that they match conventional gain functions. In addition, the joint temporal dynamics between the SNR estimate and the spectral gain function is investigated to improve the performance of the speech enhancement scheme. As the widely-used decision-directed (DD) a priori SNR estimate has a well-known one-frame delay that leads to the degradation of speech quality, a modified a priori SNR estimator is proposed for the DD approach to overcome this delay. Evaluations are performed by utilizing the objective evaluation metric that measures the trade-off between the noise reduction, the speech distortion and the musical noise in the enhanced signal. The results are compared using the PESQ and the SNRseg measures as well as subjective listening tests. Simulation results show that the proposed gain function, which can flexibly model exponential distributions, is a potential alternative speech enhancement gain function. [Copyright &y& Elsevier]

Adaptive modulation techniques in wireless communications are reactive ways designed in communication systems to thrive in unpredictable channel environments. The attractive use of adaptive communications will prove to bring more robustness and flexibility compared to fixed modulation schemes. In order for adaptive modulation to work correctly, it requires an accurate estimation of the channel condition at the receivers' end to make decisions and take action. Channel state information (CSI) has several of other uses in wireless communication systems. Accordingly, a communication link which adapts the degree of modulation scheme according to the estimated signal-to-noise ratio (SNR) values is proposed. The system estimates the current channel condition in the form of CSI and feedback to the transmitter. Hence, the objective of the adaptive system is to stay opportunistic in favourable circumstances while achieving acceptable quality margin in a time-varying communication link. In this paper, the overall system is measured using metrics of spectral efficiency and average bit error rate. Monte Carlo simulations of different signals and channel conditions corroborate our analysis and discussion. [ABSTRACT FROM AUTHOR]

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]

Abstract: Most noise reduction algorithms rely on obtaining reliable estimates of the SNR of each frequency bin. For that reason, much work has been done in analyzing the behavior and performance of SNR estimation algorithms in the context of improving speech quality and reducing speech distortions (e.g., musical noise). Comparatively little work has been reported, however, regarding the analysis and investigation of the effect of errors in SNR estimation on speech intelligibility. It is not known, for instance, whether it is the errors in SNR overestimation, errors in SNR underestimation, or both that are harmful to speech intelligibility. Errors in SNR estimation produce concomitant errors in the computation of the gain (suppression) function, and the impact of gain estimation errors on speech intelligibility is unclear. The present study assesses the effect of SNR estimation errors on gain function estimation via sensitivity analysis. Intelligibility listening studies were conducted to validate the sensitivity analysis. Results indicated that speech intelligibility is severely compromised when SNR and gain over-estimation errors are introduced in spectral components with negative SNR. A theoretical upper bound on the gain function is derived that can be used to constrain the values of the gain function so as to ensure that SNR overestimation errors are minimized. Speech enhancement algorithms that can limit the values of the gain function to fall within this upper bound can improve speech intelligibility. [Copyright &y& Elsevier]

Signal to noise ratio (SNR) estimators are required for many radio engineering applications. In this paper, a SNR estimator based on the first and second order moments is derived and examined for constant envelope modulations over additive white Gaussian noise (AWGN) channel. Firstly, a modification method is proposed to reduce the bias of conventional first and second order moments based SNR estimator. Then, in order to reduce hardware implementation complexity, multi segments of cubic polynomial are utilized to approximate the nonlinear inverse function of the proposed SNR estimator. The approximate expression is very precise in the SNR range from -10dB to 20 dB as illustrated by numerical simulation results. Besides, practical hardware circuit is proposed for FPGA implementation. Simulation results show that the proposed SNR estimator has the lower normalized bias and variance when compared with two other classic estimators. [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]

Abstract: Blind noise variance and SNR estimators based on information theoretic criteria for orthogonal frequency division multiplexing (OFDM) systems are proposed in this paper. By using the redundancy induced by the cyclic-prefix, the unknown channel order is estimated via minimum length description (MDL) or Akaike information criterion (AIC), and the noise variance and SNR can be obtained subsequently. The proposed methods do not require any pilot symbols and therefore improve the spectrum efficiency. Simulation results are presented to highlight the benefit of the proposed algorithms compared with the other existing blind methods. [Copyright &y& Elsevier]

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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper considers how to estimate the average signal-to-noise ratio (SNR) for a communication system employing orthogonal non-coherent M-ARY frequency shift keying (NCMFSK), in white Gaussian noise (AWGN) and over both symbol-by-symbol fading channels and block fading channels. The proposed algorithm finds its application in a variety of applications including a cooperative transmission system, which is the main motivation behind this study. The maximum likelihood estimator and one using data statistics have been derived and simulated for various scenarios including data-aided, non-data aided and joint estimation using both the data and pilot sequences. We also derive the Cramer-Rao bound for the estimators in the case of Rayleigh fading channels. The results show that for a particular region of interest (e.g. high SNR or low SNR) and depending upon the availability of pilot sequence, a particular SNR estimation scheme is suitable. [ABSTRACT FROM AUTHOR]

Knowing accurate noise variance and signal power is crucial to most spectrum-sensing algorithms such as energy detection, matched filter detection, and cyclostationary detection. In this paper, we consider a practical scenario when these two parameters are unknown and are needed to be estimated before the spectrum sensing. This task is non-trivial without knowing the status of the primary user, and we categorize the related spectrum sensing as a blind one. We develop the estimation algorithms for unknown parameters by exploiting the signal constellation of the primary user. Three different parameter estimators that do not require any training are then proposed based on the moments of the received signals. Since the secondary user may not know the primary user's signal constellation, we develop a robust approach that approximates a finite quadrature amplitude modulation (QAM) constellation by a continuous uniform distribution. We also derive the modified Cramer-Rao bound (CRB) for noise variance estimation. Then the optimal moment pair is found from minimizing the mean squared error (MSE) of the signal-to-noise ratio (SNR). The method of choosing the spectrum sensing threshold by taking into consideration the estimation error is also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we derive for the first time analytical expressions for the exact Cramer-Rao lower bounds on the variance of unbiased non-data-aided (NDA) signal-to-noise ratio (SNR) estimators of square QAM-modulated signals. The channel is assumed to be constant over the observation interval and the received signal is supposed to be corrupted by additive white Gaussian noise (AWGN). The derived expressions corroborate previous attempts to numerically compute the considered CRLBs. It will be shown that the NDA CRLBs differ widely from one modulation order to another especially at moderate SNR levels. [ABSTRACT FROM PUBLISHER]