NDA SNR and CRLB Estimation Over MISO with STBC Channels

2012 ◽  
Vol 8 (4) ◽  
pp. 1-16
Author(s):  
Ahmed M. Almradi ◽  
Sohail A. Dianat
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Single Output ◽  
Alamouti Coding ◽  
Single Input ◽  
Noise Ratio ◽  
Modulated Signals

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.

STOCHASTIC RESONANCE IN CHUA’S CIRCUIT DRIVEN BY AMPLITUDE OR FREQUENCY MODULATED SIGNALS

1994 ◽  
Vol 04 (02) ◽  
pp. 441-446 ◽  
Author(s):  
V.S. ANISHCHENKO ◽  
M.A. SAFONOVA ◽  
L.O. CHUA
Keyword(s):  
Numerical Simulation ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Point Of View ◽  
Chua’S Circuit ◽  
Signal Distortion ◽  
Signal To Noise ◽  
Chua's Circuit ◽  
Noise Ratio ◽  
Modulated Signals

Using numerical simulation, we establish the possibility of realizing the stochastic resonance (SR) phenomenon in Chua’s circuit when it is excited by either an amplitude-modulated or a frequency-modulated signal. It is shown that the application of a frequency-modulated signal to a Chua’s circuit operating in a regime of dynamical intermittency is preferable over an amplitude-modulated signal from the point of view of minimizing the signal distortion and maximizing the signal-to-noise ratio (SNR).

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

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.

scholarly journals PERFORMANCE EVALUATION OF RELAYING SCHEMES FOR WIRELESS COMMUNICATION SYSTEMS

2013 ◽  
pp. 157-161
Author(s):  
MANISHA CRASTO BRAGANC ◽  
HASANALI G. VIRANI ◽  
SHAILESH KHANOLKAR
Keyword(s):  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Multiple Input Multiple Output ◽  
Amplify And Forward ◽  
Decode And Forward ◽  
Wireless System ◽  
Single Input Single Output ◽  
Single Output ◽  
Alamouti Coding

The evaluation of MIMO (multiple-input multiple-output) Relay wireless system is carried out and compared against the performance of a SISO (single-input single-output) Relay wireless system. The encoding scheme used in MIMO is Alamouti coding and decoding is done by the Maximum Likelihood (ML) detector. A comparison is made between the SISO non-regenerative amplify-and-forward (AF) and regenerative decode-and-forward (DF) relaying schemes. The plots of bit error rate (BER) versus signal to noise ratio (SNR) are simulated by incorporating Rayleigh fading condition in the presence of additive white Gaussian noise(AWGN) using MATLAB.

An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding

2021 ◽  
Vol 51 ◽  
pp. 117-129
Author(s):  
Pinjala N. Malleswari ◽  
Ch. Hima Bindu ◽  
K. Satya Prasad
Keyword(s):  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Ecg Signal ◽  
Noise Power ◽  
Signal To Noise ◽  
Noise Elimination ◽  
Electrocardiogram Signals ◽  
Conventional Methods ◽  
Noise Ratio ◽  
Power Line Interference

Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707, 10.7181, 16.2824, 0.9874 and 10.7245 for the proposed EMD-DWT approach, respectively, by removing AWGN noise. Similarly BW noise and PLI are removed from the ECG signal by calculating the same quality metrics. The proposed methodology has lower RMSE and PRD values, higher SNR, PSNR and CC values than the conventional methods.

scholarly journals Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaojuan Xie ◽  
Shengliang Peng ◽  
Xi Yang
Keyword(s):  
Deep Learning ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Spectrum Management ◽  
Signal To Noise ◽  
Estimation Errors ◽  
Snr Estimation ◽  
Low Snr ◽  
Noise Ratio

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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