scholarly journals Modulation Classification of MFSK Modulated Signals Using Wavelet Decomposition

2018 ◽  
pp. 36-40
Author(s):  
Burcu Baris ◽  
Damla Gurkan Kuntalp ◽  
Mehmet Emre Cek
Keyword(s):  
Carrier Frequency ◽  
Communication Systems ◽  
Wavelet Decomposition ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Modulation Classification ◽  
Frequency Range ◽  
Intelligent Communication

In this study,a wavelet decomposition based method is proposed for determining the modulation type of the incoming signal to the receiver which is one of the important problems in intelligent communication systems. In this method, it is aimed to design the transmitted signal for determining the type of Mary FSK modulated signal and to detect the energy in each frequency band by using Discrete Wavelet Transform (DWT). For this, standard deviations in the lower bands are as features. In order to evaluate the performance of the classifier, simulation studies have been performed at different signal-to-noise ratio (SNR) levels. When the results for different frequency settings, i.e. carrier frequency and frequency range, it is seen that the classifier using the K-means clustering algorithm has a higher correct classification performance than the results reported in the literature when the suitable carrier frequency and frequency range are selected.

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 PERFORMANCE EVALUATION OF SINGLE CARRIER FREQUENCY DOMAIN EQUILIZER (SC-FDE) OVER IMPULSIVE NOISE CHANNEL

2019 ◽  
Vol 11 (2) ◽  
pp. 270-277
Author(s):  
Hussein Abdullah Leftah ◽  
Husham Lateef Swadi
Keyword(s):  
Frequency Domain ◽  
Carrier Frequency ◽  
Communication Systems ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Single Carrier ◽  
Noise Ratio ◽  
Error Floors

Impulsive noise is considered as one of the major source of disturbance in the state-of-the-art multicarrier (MC) communication systems. Therefore, several techniques are being constantly proposed to eliminate the effect of such noise. In this work, a time domain matrix interleaved is compiled with a single carrier frequency domain equalizer (SC-FDE) is proposed to reduce the deleterious effects of impulsive noise. A mathematical model for the proposed scheme is also presented in this paper. Simulation results show that the proposed technique superiors the interleaved multicarrier system where the proposed scheme can completely avoid the error floors not only at high signal-to-noise ratio (SNR) but also at heavily distributed impulsive noise. The bit-error-rate (BER) of the alternative proposed scheme decreases as the signal-to-noise ratio (SNR) increases whereas the BER of the standard system suffers from error-floors with a constant BER at about 10-3 for about 8 dB SNR for medium and heavily impulsive noise.

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).

scholarly journals Blind Identification of Convolutional Encoder Parameters

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shaojing Su ◽  
Jing Zhou ◽  
Zhiping Huang ◽  
Chunwu Liu ◽  
Yimeng Zhang
Keyword(s):  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Convolutional Codes ◽  
Soft Decision ◽  
Decision Systems ◽  
Correlation Attack ◽  
Intelligent Communication ◽  
Convolutional Encoder ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder parameters in hard-decision situations. However, soft-decision systems are applied more and more as the improvement of signal processing techniques. In this paper we propose a method to utilize the soft information to improve the recognition performances in soft-decision communication systems. Besides, we propose a new recognition method based on correlation attack to meet low signal-to-noise ratio situations. Finally we give the simulation results to show the efficiency of the proposed methods.

scholarly journals An Efficient Carrier Synchronization Scheme for Demodulation Systems

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2942
Author(s):  
Wanru Hu ◽  
Zhugang Wang ◽  
Ruru Mei ◽  
Meiyan Lin
Keyword(s):  
Carrier Frequency ◽  
Communication Systems ◽  
Carrier Frequency Offset ◽  
Initial Conditions ◽  
Signal To Noise Ratio ◽  
Linear Interpolation ◽  
Hardware Complexity ◽  
Carrier Synchronization ◽  
Symbol Rate ◽  
Synchronization Scheme

A simple data-aided carrier synchronization scheme is proposed for variable modulation (VM) communication systems under the initial conditions of a low signal-to-noise ratio (SNR) and normalized carrier frequency offset (CFO) symbol rate of 20%. The proposed carrier synchronization scheme is simplified into two steps; a reconfigurable L&R (RLR) algorithm and pilot-aided (PA) phase linear interpolation algorithm is applied for carrier frequency recovery (CFR) and carrier phase recovery (CPR), respectively. Furthermore, the autocorrelation values of multi-pilot blocks are superimposed to improve the accuracy of the CFR algorithm, and the algorithm formulas are decomposed and modularized to simplify the implementation complexity of the RLR algorithm. Simulation results show that the RLR algorithm can track and lock the CFO up to a 33.2% symbol rate and reduce the CFO to 0.024%. The bit error rate (BER) performance of the carrier synchronization scheme almost coincides with the theoretical curve results. Comparison of hardware complexity shows that the multiplication resource consumption can be reduced by at least 72.47%.

scholarly journals Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics

2020 ◽  
Vol 10 (2) ◽  
pp. 588
Author(s):  
Sang Hoon Lee ◽  
Kwang-Yul Kim ◽  
Yoan Shin
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Learning ◽  
Computational Complexity ◽  
Communication Systems ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Modulation Classification ◽  
Automatic Modulation Classification

Recently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) have been studied to improve the AMC performance. However, since the AMC process should be operated in real time, the computational complexity must be considered low enough. Furthermore, there is a lack of research to consider the complexity of the AMC process using the data-mining method. In this paper, we propose a correlation coefficient-based effective feature selection method that can maintain the classification performance while reducing the computational complexity of the AMC process. The proposed method calculates the correlation coefficients of second, fourth, and sixth-order cumulants with the proposed formula and selects an effective feature according to the calculated values. In the proposed method, the deep learning-based AMC method is used to measure and compare the classification performance. From the simulation results, it is indicated that the AMC performance of the proposed method is superior to the conventional methods even though it uses a small number of features.

Research of Terahertz Spectra of Terbutaline Sulfate Based on Wavelet De-Noising

2011 ◽  
Vol 63-64 ◽  
pp. 327-332
Author(s):  
Xiai Chen ◽  
Ping Jie Huang ◽  
Di Bo Hou ◽  
Xu Sheng Kang ◽  
Guang Xin Zhang ◽  
...  
Keyword(s):  
Time Domain ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Discrete Wavelet ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Terbutaline Sulfate ◽  
Soft Threshold

Terahertz spectra of terbutaline sulfate in the range of 0.2 to 2.2 THz was obtained by THz time-domain spectroscopy. The discrete wavelet transform was applied to de-noising terahertz waveforms. The signal was decomposed into five layers by wavelet decomposition, and then the high-frequency noise signal was eliminated by wavelet reconstruction. Another try was through calculating the standard deviation of the noise signal by the 1-th level signals which got from wavelet decomposition, and then the soft threshold and hard threshold de-noising method was employed respectively. The robustness of these wavelet de-noising methods was testified in this paper, and the absorption and refraction spectra of terbutaline sulfate were got at last. The result of experiment indicts that wavelet can enhance the signal to noise ratio of system and this paper provides a new way for the detection of terbutaline sulfate.

scholarly journals Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction

2021 ◽  
Vol 15 ◽  
Author(s):  
Feifei Qi ◽  
Wenlong Wang ◽  
Xiaofeng Xie ◽  
Zhenghui Gu ◽  
Zhu Liang Yu ◽  
...  
Keyword(s):  
Wavelet Decomposition ◽  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Spatial Filtering ◽  
Classification Performance ◽  
Single Trial ◽  
Eeg Signals ◽  
Orthogonal Wavelet ◽  
Spatial Filters ◽  
Eeg Classification

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.

Evaluvation of Multiresolution Watermarking Algorithm

2017 ◽  
Vol 7 (8) ◽  
pp. 85
Author(s):  
S. Thabasu Kannan ◽  
S. Azhagu Senthil
Keyword(s):  
Digital Watermarking ◽  
Wavelet Transformation ◽  
Signal To Noise Ratio ◽  
Rapid Development ◽  
Threshold Value ◽  
Discrete Wavelet ◽  
Digital Production ◽  
Common Framework ◽  
Sampling Operation ◽  
Watermarking Scheme

Now-a-days watermarking plays a pivotal role in most of the industries for providing security to their own as well as hired or leased data. This paper its main aim is to study the multiresolution watermarking algorithms and also choosing the effective and efficient one for improving the resistance in data compression. Computational savings from such a multiresolution watermarking framework is obvious. The multiresolutional property makes our watermarking scheme robust to image/video down sampling operation by a power of two in either space or time. There is no common framework for multiresolutional digital watermarking of both images and video. A multiresolution watermarking based on the wavelet transformation is selected in each frequency band of the Discrete Wavelet Transform (DWT) domain and therefore it can resist the destruction of image processing.   The rapid development of Internet introduces a new set of challenging problems regarding security. One of the most significant problems is to prevent unauthorized copying of digital production from distribution. Digital watermarking has provided a powerful way to claim intellectual protection. We proposed an idea for enhancing the robustness of extracted watermarks. Watermark can be treated as a transmitted signal, while the destruction from attackers is regarded as a noisy distortion in channel.  For the implementation, we have used minimum nine coordinate positions. The watermarking algorithms to be taken for this study are Corvi algorithm and Wang algorithm. In all graph, we have plotted X axis as peak signal to noise ratio (PSNR) and y axis as Correlation with original watermark. The threshold value ά is set to 5. The result is smaller than the threshold value then it is feasible, otherwise it is not.

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