Signal-to-noise Ratio Estimation Technique Research Articles

An SNR Estimation Technique Based on Deep Learning

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.

SNR Estimation for Multi-dimensional Cognitive Receiver under Correlated Channel/Noise

In addition to Spectrum Sensing (SS) capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals at the CR receiver is crucial in order to adapt its coverage area dynamically using underlay techniques. In practical scenarios, channel and noise may be correlated due to various reasons and SNR estimation techniques with the assumption of white noise and uncorrelated channel may not be suitable for estimating the primary SNR. In this paper, firstly, we study the performance of different eigenvalue-based SS techniques in the presence of channel or/and noise correlation. Secondly, we carry out detailed theoretical analysis of the signal plus noise hypothesis to derive the asymptotic eigenvalue probability distribution function (a.e.p.d.f.) of the received signal's covariance matrix under the following two cases: (i) correlated channel and white noise, and (ii) correlated channel and correlated noise, which is the main contribution of this paper. Finally, an SNR estimation technique based on the derived a.e.p.d.f is proposed in the presence of channel/noise correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be reliably estimated when the CR sensing module is aware of the channel/noise correlation.

A Dual-Microphone Algorithm That Can Cope With Competing-Talker Scenarios

This paper introduces a novel technique for signal-to-noise ratio (SNR) estimation for scenarios where two closely-spaced microphones are available. The proposed technique utilizes the real and imaginary parts of the coherence function between the input signals to estimate the SNR without assuming prior knowledge of the noise statistics. The corresponding dual-microphone speech enhancement algorithm utilizes a Wiener filter as a gain function constructed using the SNR values computed by the coherence function. Since the proposed SNR estimation technique does not require access to noise statistics, it can be applied in situations where interfering speakers are present. An adaptive speech reception threshold (SRT) test was used to assess the intelligibility of speech processed by the proposed algorithm in scenarios where one or two interfering talkers were present in anechoic and reverberant conditions. Intelligibility listening tests were conducted with both normal-hearing (NH) and cochlear implant (CI) listeners. Results revealed significant improvements in intelligibility and quality over a (baseline) fixed directional algorithm and a well-established beamformer algorithm. In a nearly anechoic room with competing talkers, the improvement in SRT obtained relative to the directional microphone ranged from 5-10 dB, while the improvement obtained by the beamformer was about 2 dB. In reverberant environments, the improvement in SRT remained high (4-7 dB) at T60 = 220 ms, and decreased to 1-2 dB at T60 = 465 ms. Overall, the proposed algorithm provided significant benefits in intelligibility in anechoic and mildly reverberant environments making it suitable for hearing aid and cochlear implant applications.

Statistical model based SNR estimation method for speech signals

The performance of speech enhancement algorithms to a large extent is related to the employed signal-to-noise ratio (SNR) estimation techniques. Many of the existing SNR estimation techniques are based on approaches that require either an experimentally pre-specified weighting factor or prior assumptions of the parameters in the signal model. In this reported work, a closed form SNR estimator is derived by modelling the noisy speech signal as a generalised normal-Laplace distribution and estimating the variance of the signal and variance of the noise using high-order sample moments. The performance of the proposed technique is tested using real speech signals and compared with the well-known eigenvalue method.

Moment-based SNR estimation over linearly-modulated wireless SIMO channels

In this paper, we develop a new method for signal-to-noise ratio (SNR) estimation when multiple antenna elements receive linearly-modulated signals in complex additive white Gaussian noise (AWGN) spatially uncorrelated between the antenna elements. We also derive extensions of other existing moment-based SNR estimators to the single-input multiple-output (SIMO) configuration. The new SIMO SNR estimation technique is non-data-aided (NDA) since it is a moment-based method and does not rely, therefore, on the a priori knowledge or detection of the transmitted symbols; it does not require the a priori knowledge of the modulation type or order. The new method is shown by Monte Carlo simulations to clearly outperform the best NDA moment-based SNR estimation methods in terms of normalized root mean square error (NRMSE) over QAM-modulated transmissions, namely the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> method and the estimators referred to, in this paper, as the GT and the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> methods, even when we extend them to the SIMO configuration.

낮은 계산 복잡도를 갖는 Linear Prediction 기반의 SNR 추정 기법

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

Blind SNR Estimation with Coherent Function for OFDM Systems

In OFDM receivers, an accurate signal-to-noise ratio (SNR) estimation is desirable for all sorts of operations involved in high-performance baseband demodulation. In this work, conventional SNR estimation techniques are investigated. Next, a blind SNR estimation scheme for the phase-shift keying (PSK) signals based on the coherence function is proposed. The proposed method is non-data-aided (NDA) and hence bandwidth-efficient. Simulations confirm that the proposed method has the best performance from moderate to high SNRs both in AWGN and dispersive channels; however, the proposed method has worse performance when SNRs are low.