Harmonics-Based One-Bit SNR Estimation

Coherent Doppler wind lidar (CDWL) is a critical technology in wind measurement. However, the large volume of data generated during detection poses a significant challenge for data acquisition and processing systems. While 1-bit sampling using a comparator (CMP) has been proposed to reduce FPGA resource consumption, this approach generally leads to degradation of the carrier-to-noise ratio (CNR) and a noticeable reduction in detection range. A novel approach, to our knowledge, termed the 1-bit sampling via analog-to-digital converter (ADC), is proposed in this paper. This method offers the potential to achieve comparable FPGA resource utilization while delivering improved CNR and an extended detection range relative to conventional CMP-based 1-bit sampling. To evaluate the proposed method, we designed and implemented a data acquisition module (DAQ) that supports three sampling modes: multi-bit ADC sampling, 1-bit ADC sampling, and 1-bit CMP sampling. Experimental validation was carried out using a 1.5 µm CDWL system. The results demonstrate that the proposed 1-bit ADC sampling achieves higher CNR performance and a longer detection range than 1-bit CMP sampling while achieving slightly lower detection range than multi-bit ADC sampling. It is anticipated that this work will provide valuable insights for the research community and contribute to the advancement of future CDWL systems.

In this article, the authors study the convergence of an iterative algorithm for the joint estimation of the signal-to-noise ratio (SNR) and the transmission channel in orthogonal frequency division multiplexing context. At each step of the algorithm, the authors use the minimum-mean-square error (MMSE)-based SNR estimation, which feeds the linear MMSE channel estimation. Reciprocally, this efficient channel estimation is used to perform the SNR estimation. The authors provide a proof of convergence of the algorithm to a single value. Furthermore, we derive an accurate approximation of the bias of the estimation. Simulations show that the algorithm converges quickly and verifies the theoretical results. They also show the efficiency of both SNR and channel estimation. By comparing with the existing methods, the authors show that the tradeoff between the number of required pilots in the preamble and the performance of the SNR estimation were improved. Furthermore, for a fixed bit error rate, the SNR gap between the proposed channel estimation and the perfect one is <0.5 dB.

In this paper, an approach to estimate signal to noise ratio (SNR) and effective number of bits (ENOB) in nonideal multi-bit stages of pipelined analog to digital converters (ADCs) is presented. The most significant error sources in multistage ADCs are the capacitor mismatch and the finite and imprecise gain of amplifier. Output voltage of each stage in pipelined ADC is modeled by an ideal and a nonideal output, where nonideal output is the error due to circuit imperfections in each stage. Using an appropriate model, the SNR and ENOB due to circuit nonidealities and in terms of standard deviation of random errors are calculated. Simulation results show the accuracy of the analytical proposed approach in estimation of SNR and ENOB in multi-bit per stage pipelined converters.

Many existing signal-to-noise ratio (SNR) estimators were designed and evaluated for conventional one-hop communications systems. However, for a relaying system, it is the end-to-end SNR that determines the system performance. In this paper, we will fill this gap by evaluating the performances of the existing SNR estimators in a dual-hop relaying system used for each hop. The probability density functions of the SNR estimators are first derived, whose parameters are fitted as functions of the sample size and the true value of SNR. Using them, the cumulative distribution functions of the end-to-end SNR and the bit error rate performance for a relaying system are derived. Numerical results show that the squared signal-to-noise variance estimator has the best performance for small SNRs and the second-order fourth-order moments estimator has the best performance for large SNRs, while the signal-to-variation ratio estimator has the worst performance, among the existing SNR estimators, for AF relaying systems.

This paper discusses the problem of Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation of Binary Phase Shift keying (BPSK) modulated signals using the Expectation Maximization (EM) Algorithm. In addition, the Cramer-Rao Lower Bounds (CRLB) for the estimation of Data Aided (DA) and Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation is derived. Multiple Input Single Output (MISO) channels with Space Time Block Codes (STBC) is used. The EM algorithm is a method that finds the Maximum Likelihood (ML) solution iteratively when there are unobserved (hidden or missing) data. Extension of the proposed approach to other types of linearly modulated signals in estimating SNR is straight forward. The performance of the estimator is assessed using the NDA CRLBs. Alamouti coding technique is used in this paper with two transmit antennas and one receive antenna. The authors’ assumption is that the received signal is corrupted by additive white Gaussian noise (AWGN) with unknown variance, and scaled by fixed unknown complex channel gain. Monte Carlo simulations are used to show that the proposed estimator offers a substantial improvement over the conventional Single Input Single Output (SISO) NDA SNR estimator due to the use of the statistical dependences in space and time. Moreover, the proposed NDA SNR estimator works close to the NDA SNR estimator over Single Input Multiple Output (SIMO) channels.

This paper introduces novel two-channel a priori Signal-to-Noise Ratio (SNR) estimators for use in frequency-domain speech enhancement algorithms. The SNR estimation is based on statistics of the noisy phase difference between two microphones in each frequency bin. Namely, the corresponding probability distribution is derived assuming a complex Gaussian model, and is written in terms of the SNR only. This shifts the two-channel SNR estimation problem into a classical statistical parameter estimation problem, which we propose to solve via an online version of the Method of Moments. The usefulness of the resulting approach is demonstrated in a realistic speech enhancement experiment, where a simple Wiener-based solution is objectively found to perform competitively when compared with other recent two-channel noise reduction techniques.

In this paper we have estimated signal to noise ratio (SNR) of active users in uplink scenario of CDMA systems blindly. We have not any knowledge about PN (pseudo noise) code, frequency, and timing of received signals. In this way first we synchronized received signal of desired user and then we use subspace method for SNR estimation of synchronized user. This type of SNR estimation is classified as non data aided (NDA) methods. Our synchronization is based on maximizing of covariance matrix Frobenius norm. After that by estimating PN code of strongest signal we are able to estimate transmitted signal data, and by using estimated SNR, we can eliminate this signal from received signal by using SIC (serial interference canceller). By repetition this cycle for each user we can estimate SNR of active users in uplink scenario. Also, in this paper we use minimum description length (MDL) criteria for estimating signal subspace dimension. Standard divisions of estimated SNR show that good agreement with exact SNR for all users.

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.

This paper proposes a highly accurate signal to noise ratio (SNR) estimation method for adaptive modulation and coding (AMC) by learning power spectral density (PSD) images with a convolutional neural network (CNN). Accurate SNR estimation is indispensable for adaptive control schemes such as AMC. Proposed method trains the CNN using PSD images and the corresponding SNR value as a teacher signal, and outputs the SNR in response to an input of unknown PSD image. Once trained, the SNR can be estimated with high speed and high accuracy, so AMC performance can be improved. Furthermore, since PSD is hardly affected by the Doppler shift, proposed method is resistant to high-speed environment. Compared with the previously proposed method which estimates SNR from a PSD data using a neural network, the proposed method can improve the estimation accuracy, bit error rate (BER) and throughput performance when AMC is applied.

In wireless orthogonal frequency division multiplexing (OFDM) systems, the knowledge of signal-to-noise ratio (SNR) plays an important role for system optimization. Most of the exiting literatures have studied the SNR estimation for perfect synchronization in additive white Gaussian noise (AWGN) channels, or in frequency selective channels. However, the realistic channels are always doubly selective, and there may exist some residual symbol timing offset (STO) and carrier frequency offset (CFO) after coarse synchronization in the time domain. In this paper, we take these into account and propose a time-domain average SNR estimation scheme using one OFDM training symbol (preamble) which has been divided into multiple parts with equal length. Compared with the timedomain low-complexity SNR estimator (TLSE), our proposed time-domain preamble-based SNR estimator (TPSE) can not only be implemented in the presence of STO and CFO, but also generate more accurate SNR estimation for the channels with large Doppler shift. The estimated SNR value based on our scheme in the time-domain can be further reused to improve the ensuing CFO estimation and fine synchronization efficiently.

A method by which a popular signal-to-noise ratio (SNR) estimator can be reconfigured to yield improved performance is proposed. The reconfiguration consists of partitioning the symbol interval into a larger (but even) number of subdivisions than the two that have been traditionally suggested for this estimator but still processing the observables in successive pairs. The optimum number of subdivisions is shown to depend on the SNR range in which the true SNR lies; however, we also show that these SNR regions can be significantly widened with very little loss in performance. Most important is the fact that with this reconfiguration, the SNR estimator tracks the Cramer-Rao bound on the variance of the estimator over the entire range of SNR values.

An adaptive signal-to-noise ratio estimator in mobile communication channels

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The aim is to correctly estimate SNR values from 0 to 30 dB, a range that is both practical and crucial for speaker identification systems. Additive white Gaussian noise and pink noise are investigated. The best feature for both white and pink noise is the vector of reflection coefficients which achieves an average SNR estimation error of 1.6 dB and 1.85 dB for white and pink noise respectively. Combining the estimates of 4 features lowers the error for white noise to 1.46 dB and for pink noise to 1.69 dB

Estimating the Signal to Noise Ratio (SNR) of the Primary Users' (PUs) signals over a wideband spectrum accurately is crucial in order to fully exploit an under-utilized primary spectrum using underlay Cognitive Radio (CR) techniques. In this context, we study an SNR estimation problem for a wideband CR under practical correlated scenarios in compressive settings. We carry out detailed theoretical analysis for the considered scenarios and then derive the expressions for the asymptotic eigenvalue probability distribution function (aepdf) of the measured signal's covariance matrix using asymptotic Random Matrix Theory. Subsequently, based on the derived aepdfs, we present a technique to estimate the PU SNR over a wideband spectrum with compressive measurements. The performance of the proposed technique is evaluated in terms of normalized Mean Square Error (MSE) and it is shown that the SNR of the PU signals over the wideband spectrum can be reliably estimated using the proposed technique.

Visible light communication (VLC) has emerged as a promising technology for future sixth-generation (6 G) communications. Estimating and predicting the impairments, such as turbulence and free space signal scattering, can help to construct flexible and adaptive VLC networks. However, the monitoring of impairments of VLC is still in its infancy. In this Letter, we experimentally demonstrate a deep-neural-network-based signal-to-noise ratio (SNR) estimation scheme for VLC networks. A vision transformer (ViT) is first utilized and compared with the conventional scheme based on a convolutional neural network (CNN). Experimental results show that the ViT-based scheme exhibits robust performance in SNR estimation for VLC networks compared to the CNN-based scheme. Specifically, the ViT-based scheme can achieve accuracies of 76%, 63.33%, 45.33%, and 37.67% for 2-quadrature amplitude modulation (2QAM), 4QAM, 8QAM, and 16QAM, respectively, against 65%, 57.67%, 41.67%, and 34.33% for the CNN-based scheme. Additionally, data augmentation has been employed for achieving enhanced SNR estimation accuracies of 95%, 79.67%, 58.33%, and 50.33% for 2QAM, 4QAM, 8QAM, and 16QAM, respectively. The effect of the SNR step size of a contour stellar image dataset on the SNR estimation accuracy is also studied.