An Improved Acquisition Method for High-Order BOC-Modulated Signals Based on Fractal Geometry

2013 ◽  
Vol 846-847 ◽  
pp. 1185-1188 ◽  
Author(s):  
Hua Bing Wu ◽  
Jun Liang Liu ◽  
Yuan Zhang ◽  
Yong Hui Hu
Keyword(s):  
Fractal Geometry ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
High Order ◽  
Main Peak ◽  
Signal To Noise ◽  
Binary Offset Carrier ◽  
Simulation Results ◽  
Modulated Signals ◽  
Acquisition Method

This paper proposes an improved acquisition method for high-order binary-offset-carrier (BOC) modulated signals based on fractal geometry. We introduced the principle of our acquisition method, and outlined its framework. We increase the main peak to side peaks ratio in the BOC autocorrelation function (ACF), with a simple fractal geometry transform. The proposed scheme is applicable to both generic high-order sine-and cosine-phased BOC-modulated signals. Simulation results show that the proposed method increases output signal to noise ratio (SNR).

A Triple-Loop Tracking Method for High-Order BOC-Modulated Signals

2014 ◽  
Vol 568-570 ◽  
pp. 1326-1330
Author(s):  
Hua Bing Wu ◽  
Jun Liang Liu ◽  
Yong Hui Hu
Keyword(s):  
High Order ◽  
Main Peak ◽  
Binary Offset Carrier ◽  
Tracking Method ◽  
Simulation Results ◽  
Modulated Signals

This paper proposes a triple-loop trackingmethod for high-order binary-offset-carrier (BOC)modulated signals. We introduced the characteristics of high-order BOC signals, and then describedthe principle of the triple-loop tracking method, and outlined its framework. The proposed scheme isapplicable to both generic high-order sine- and cosine-phased BOC-modulated signals. The methodremoves the threats brought by the side-peaks ambiguities, while keeping the same sharp correlationof the main peak, thus, allowing for better tracking precision. Simulation results show the validity ofour method in improving the system tracking performances.

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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).

Highly Accurate Frequency Interpolation of Apodized FFT Magnitude-Mode Spectra

1998 ◽  
Vol 52 (1) ◽  
pp. 134-138 ◽  
Author(s):  
Yutaka Goto
Keyword(s):  
Simple Formula ◽  
Previous Investigation ◽  
Signal To Noise Ratio ◽  
Damping Ratio ◽  
Frequency Error ◽  
Signal To Noise ◽  
Window Functions ◽  
Optimal Values ◽  
Simulation Results ◽  
True Frequency

“Generalized” interpolation (called GIα here) of fast Fourier transform (FFT) spectra apodized by a family of sinα ( X) windows has previously been proposed. The GIα gives the highly accurate interpolated frequency by calculating the simple formula of frequency determination with the use of two squared ratios between three magnitudes nearest to the peak maximum on the apodized FFT spectrum. Although the value of window parameter α, limited to integer values, has been used for the GIα, we show in the present paper that the GIα with a real α value also gives an extremely good estimate of the true frequency from the sinα ( X)-apodized spectra. Thus, we intend to apply the GIα with the optimal values of α to FFT spectra apodized by any other window functions that are often used in Fourier spectroscopy. Simulation results show that the GIα is easier and more accurate than the KCe interpolation, which uses a family of interpolating functions [ KCe(ω) = ( aω2 + bω + c)e] proposed by Keefe and Comisarow. Finally, in the presence of noise we examine effects of damping and windowing on the frequency interpolation of FFT spectra. Because damping and windowing reduce the signal-to-noise ratio (SNR), we define anew the relative SNR by the ratio of the SNR of the apodized spectrum of a damped sinusoid to the SNR of the unapodized spectrum of an undamped sinusoid. Numerical calculation shows that the relative SNR varies, owing to damping rather than windowing. In fact, the observed frequency error roughly increases as the damping ratio increases for any window functions, as is expected from our previous investigation that the frequency error based upon the GIα is inversely proportional to the SNR. However, no obvious differences between the various window functions are observed in the presence of noise.

Digital Modulation Recognition in Different Environments

2014 ◽  
Vol 1049-1050 ◽  
pp. 2084-2087 ◽  
Author(s):  
Rong Li
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Digital Modulation ◽  
Multipath Interference ◽  
Modulation Signal ◽  
Simulation Results ◽  
Recognition Success

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

scholarly journals Superposition Coded Modulation Based Faster-Than-Nyquist Signaling

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Shuangyang Li ◽  
Baoming Bai ◽  
Jing Zhou ◽  
Qingli He ◽  
Qian Li
Keyword(s):  
Bit Error Rate ◽  
Spectral Efficiency ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Coded Modulation ◽  
Signal To Noise ◽  
Data Rates ◽  
Orthogonal Signaling ◽  
Simulation Results ◽  
Constrained Capacity

A structure of faster-than-Nyquist (FTN) signaling combined with superposition coded modulation (SCM) is considered. The so-called FTN-SCM structure is able to achieve the constrained capacity of FTN signaling and only requires a low detection complexity. By deriving a new observation model suitable for FTN-SCM, we offer the power allocation based on a proper detection method. Simulation results show that, at any given spectral efficiency, the bit error rate (BER) curve of FTN-SCM lies clearly outside the minimum signal-to-noise ratio (SNR) boundary of orthogonal signaling with a larger alphabet. The achieved data rates are also close to the maximum data rates of the certain shaping pulse.

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