scholarly journals Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Qi An ◽  
Zi-shu He ◽  
Hui-yong Li ◽  
Yong-hua Li
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Estimation Accuracy ◽  
Modulation Classification ◽  
Electronic Warfare ◽  
Wireless Communication Network ◽  
Shift Keying ◽  
Analytical Formulas ◽  
Fractional Function

Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

scholarly journals Accurate Frequency Estimation Based On Three-Parameter Sine-Fitting With Three FFT Samples

2015 ◽  
Vol 22 (3) ◽  
pp. 403-416 ◽  
Author(s):  
Xin Liu ◽  
Yongfeng Ren ◽  
Chengqun Chu ◽  
Wei Fang
Keyword(s):  
Fourier Transform ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Golden Section ◽  
Least Square ◽  
Estimation Accuracy ◽  
Tone Frequency ◽  
Single Tone ◽  
Dc Component ◽  
Fitting Error

Abstract This paper presents a simple DFT-based golden section searching algorithm (DGSSA) for the single tone frequency estimation. Because of truncation and discreteness in signal samples, Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) are inevitable to cause the spectrum leakage and fence effect which lead to a low estimation accuracy. This method can improve the estimation accuracy under conditions of a low signal-to-noise ratio (SNR) and a low resolution. This method firstly uses three FFT samples to determine the frequency searching scope, then – besides the frequency – the estimated values of amplitude, phase and dc component are obtained by minimizing the least square (LS) fitting error of three-parameter sine fitting. By setting reasonable stop conditions or the number of iterations, the accurate frequency estimation can be realized. The accuracy of this method, when applied to observed single-tone sinusoid samples corrupted by white Gaussian noise, is investigated by different methods with respect to the unbiased Cramer-Rao Low Bound (CRLB). The simulation results show that the root mean square error (RMSE) of the frequency estimation curve is consistent with the tendency of CRLB as SNR increases, even in the case of a small number of samples. The average RMSE of the frequency estimation is less than 1.5 times the CRLB with SNR = 20 dB and N = 512.

scholarly journals Parameter estimation and signal detection algorithm based on adaptive capture in non-cooperative communication

2021 ◽  
Vol 336 ◽  
pp. 04002
Author(s):  
Zilong He ◽  
Peng Sun ◽  
Kexian Gong ◽  
Hua Jiang
Keyword(s):  
Parameter Estimation ◽  
Signal Detection ◽  
Cooperative Communication ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Signal Frequency ◽  
Signal Spectrum

Aiming at the problem that the frequency offset in the non-cooperative communication system causes the received signal spectrum to shift, which exceeds the passband of the matched filter and affects the subsequent demodulation, a parameter estimation and signal detection algorithm based on adaptive capture is proposed by this paper, which is more convenient for hardware implementation and consumes less resources. The algorithm is divided into three parts. Firstly, use the correlation value between the signal and the preamble sequence as the basis for frequency capture. Secondly, the frequency is accurately estimated based on the interpolation algorithm. Finally, the phase-locked loop structure is used to track the frequency according to the characteristics of the frequency gradually changing and the signal frequency offset is eliminated in the Digital Down Converter stage. It provides necessary conditions for accurate signal detection and phase estimation. The simulation results show that the algorithm has high estimation accuracy, wide esti-mation range and low complexity. It can also achieve better estimation accuracy and detection performance under low signal-to-noise ratio.

scholarly journals Automatic Digital Modulation Classification Based on Curriculum Learning

2019 ◽  
Vol 9 (10) ◽  
pp. 2171 ◽  
Author(s):  
Min Zhang ◽  
Zhongwei Yu ◽  
Hai Wang ◽  
Hongbo Qin ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Carrier Frequency Offset ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Modulation Classification ◽  
Digital Modulation ◽  
Electronic Warfare ◽  
Great Performance ◽  
Digital Modulation Classification ◽  
Test Sets ◽  
Automatic Modulation Classifier

Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with −18–20 dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare.

Automatic Modulation Identification of MPSK Signals Using Differential Signal and its Higher Order Cumulants

2011 ◽  
Vol 403-408 ◽  
pp. 2547-2551
Author(s):  
Zhan Hui Cai ◽  
Yuan Cheng Yao
Keyword(s):  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Phase Shift Keying ◽  
Eighth Order ◽  
Differential Signal ◽  
Automatic Modulation Classification ◽  
Shift Keying ◽  
Simulation Results ◽  
Intelligent Communication ◽  
Classification Probability

Automatic modulation classification plays a significant role in intelligent communication. A new method based on feature extraction is proposed for the recognition of M-ary Phase Shift Keying (MPSK) signals. As features, fourth and eighth order cumulants of the input samples and phase differential sequences were applied. It is shown that the cumulant-based features have robust anti-noise ability. Simulation results demonstrate that the correct classification probability (Pcc) with the proposed algorithm is higher than the existing approaches at low signal-to-noise ratio (SNR).

Improved SNR Estimation Technique for BPSK and QPSK Signals

2012 ◽  
Vol 239-240 ◽  
pp. 994-999
Author(s):  
Guang Zu Liu ◽  
Jian Xin Wang
Keyword(s):  
Phase Shift ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Estimation Accuracy ◽  
Estimation Technique ◽  
Phase Shift Keying ◽  
Low Snr ◽  
Mathematical Relation ◽  
Shift Keying ◽  
Binary Phase Shift Keying

To improve the estimation accuracy of non-data-aided (NDA) signal-to-noise ratio (SNR) estimators at low SNR value, A novel estimation technique for binary phase-shift keying and quadrature phase-shift keying signals in complex additive white Gaussian noise channel is proposed. The mathematical relation between SNR and the ratio of two simple statistical computations is derived, then SNR is determined by looking up a table. Its accuracy surpasses other NDA estimators, approaching closely to the Cramer-Rao lower bound at SNR > 5dB.

scholarly journals Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Changwei Ma
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
High Frequency ◽  
Fourier Coefficients ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Size Number

Discrete Fourier transform- (DFT-) based maximum likelihood (ML) algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR) increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB), which is dependent on the number of DFT points. However, its mean square error (MSE) performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR), least squares SVR (LS-SVR) can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

A Novel Symbol Rate Estimation Algorithm for Phase Modulating Signals in Wireless Communications

2013 ◽  
Vol 443 ◽  
pp. 392-396
Author(s):  
Peng Zhou ◽  
Chi Sheng Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Resolution ◽  
Phase Shift Keying ◽  
Phase Jump ◽  
Rate Estimation ◽  
Awgn Channel ◽  
Symbol Rate ◽  
Shift Keying ◽  
Simulation Results

In this paper, we proposed a new symbol rate estimation algorithm for phase shift keying (PSK) and qua drawtube amplitude modulation (QAM) signals in AWGN channel First we constructe a delay-multiplied signal, from which we obtaine the modulated information. Then we calculated the instantaneous autocorrelation of the delay-multiplied signal to pick out the phase jump. To eliminate the restriction of frequency resolution in fast Fourier transform, we performed a Chirp-Z transform to find out the exact spectral line which represente the symbol rate of the signal to be analyzed. Compared with the existing algorithms, it is a simple solution that has a better performance and accuracy in low signal-to-noise-ratio channel conditions. Simulation results show that the probability of relative estimating deviation below 0.1% reaches 100% and the average and standard variance of absolute estimation deviation are at the magnitude of 10-2 when SNR is over 2dB.

scholarly journals Multilayer Learning Network for Modulation Classification Assisted with Frequency Offset Cancellation in Satellite to Ground Link

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Guan Qing Yang
Keyword(s):  
Carrier Frequency ◽  
Carrier Frequency Offset ◽  
Frequency Offset ◽  
Training Sequence ◽  
Classification Performance ◽  
Modulation Classification ◽  
Correct Classification ◽  
Offset Cancellation ◽  
Learning Network ◽  
Hidden Layer

A multilayer learning network assisted with frequency offset cancellation is proposed for modulation classification in satellite to ground link. Carrier frequency offset greatly reduces modulation classification performance. It is necessary to cancel frequency offset before modulation classification. Frequency offset cancellation weights are established through multilayer learning network based on MSE criterion. Then the weight and hidden layer of multilayer learning network are also established for modulation classification. The hidden layers and weight are trained and tuned to combat the interference introduced by frequency offset. Compared with current modulation classification algorithm, the proposed multilayer learning network greatly improves the Probability of Correct Classification (PCC). It has been proven that the proposed multilayer learning network assisted with frequency offset has higher performance for modulation classification within the same training sequence.

scholarly journals NOISE STABILITY OF SIGNAL RECEPTION ALGORITHMS WITH MULTIPULSE PULSE-POSITION MODULATION

2018 ◽  
Vol 42 (1) ◽  
pp. 167-174 ◽  
Author(s):  
V. I. Parfenov ◽  
D. Y. Golovanov
Keyword(s):  
Communication Systems ◽  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Point Of View ◽  
Estimation Accuracy ◽  
Pulse Position Modulation ◽  
Signal To Noise ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Ratio

An algorithm for estimating time positions and amplitudes of a periodic pulse sequence from a small number of samples was proposed. The number of these samples was determined only by the number of pulses. The performance of this algorithm was considered on the assumption that the spectrum of the original signal is limited with an ideal low-pass filter or the Nyquist filter, and conditions for the conversion from one filter to the other were determined. The efficiency of the proposed algorithm was investigated through analyzing in which way the dispersion of estimates of time positions and amplitudes depends on the signal-to-noise ratio and on the number of pulses in the sequence. It was shown that, from this point of view, the efficiency of the algorithm decreases with increasing number of sequence pulses. Besides, the efficiency of the proposed algorithm decreases with decreasing signal-to-noise ratio.It was found that, unlike the classical maximum likelihood algorithm, the proposed algorithm does not require a search for the maximum of a multivariable function, meanwhile characteristics of the estimates are practically the same for both these methods. Also, it was shown that the estimation accuracy of the proposed algorithm can be increased by an insignificant increase in the number of signal samples.The results obtained may be used in the practical design of laser communication systems, in which the multipulse pulse-position modulation is used for message transmission. 

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