• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.63-68
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• 2005

In this paper, performance of a maximum-likelihood modulation classification for quadrature amplitude modulation (QAM) is studied. Unlike previous works, the relative classification performance with respect to the available modulations and performance limit with single-sample observation are presented. For those purposes, all constellations are set to have the same minimum Euclidean distance between symbols so that a smaller constellation is a subset of the larger ones. And only one sample of received waveform is used for multiple hypothesis test. As a result, classification performance is improved with increase in signal-to-noise ratio in all the experiments. Especially, when the true modulation format used in the transmitter is 4 QAM, almost perfect classification can be achieved without any additional information or observation samples. Though the possibility of false classification due to the symbols shared by subset constellations always exists, correct classification ratio of $80{\%}$ can be obtained with the single-sample observation when the true modulation formats are 16 and 64 QAM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.1019-1026
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• 1996

A modulation type classifier based on statistical moments has been successfully employed to classify PSK signals. Previously, developed Classifiers were analyzed in AWGN channel only. In this paper, a moments-based modulation type classifier to classify BPSK and QPSK signals over Rayleigh fading channel is proposed and analyzed. The moments of received signal are evaluated with the exact distribution of the received signal and a moments-based classifier is proposed. The performance evaluation of the proposed classifier in terms of the misclassification probability for BPSK and QPSK is investigated under Rayleigh fading environment.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.967-970
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• 2014

An efficient peak detection algorithm in magnitude spectrum is proposed to distinguish the M-frequency shift keying(FSK) signals from other digitally modulated signal. In addition, recognition of the modulation order estimation of FSK signals is also studied based on the fact that the magnitude spectrum of FSK signals reveals the number of peaks equal to the modulation order. When no a priori information about the signals, we utilize the histogram of the magnitude spectrum to determine the threshold which is important factor in peak detection algorithm. The simulation results show high probability of classification under 500 symbols and signal-to-noise ratio(SNR) higher than 4dB.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.257-260
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• 2017

In this letter, we propose an efficient automatic modulation recognition (AMR) method which classifies digitally modulated signals by estimating the bandwidth. In AMR, feature-based methods are widely used and the accuracy of the features is highly dependent on the number of symbols and the number of samples per symbol (NSPS). In this letter, at first, we coarsely estimate the bandwidth of the oversampled signals, and then decrease the sample rate to yield adequate NSPS. As a result, more symbols are used for AMR and the correct classification rate becomes high under the same number of samples.

• Journal of IKEEE
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• v.26 no.4
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• pp.568-576
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• 2022

Automatic modulation classification(AMC) is a core technique in Software Defined Radio(SDR) platform that enables smart and flexible spectrum sensing and access in a wide frequency band. In this study, we propose a simple yet accurate deep learning-based method that allows AMC for variable-size radio signals. To this end, we design a classification architecture consisting of two Convolutional Neural Network(CNN)-based models, namely main and small models, which were trained on radio signal datasets with two different signal sizes, respectively. Then, for a received signal input with an arbitrary length, modulation classification is performed by augmenting the input samples using a self-replicating padding technique to fit the input layer size of our model. Experiments using the RadioML 2018.01A dataset demonstrated that the proposed method provides higher accuracy than the existing methods in all signal-to-noise ratio(SNR) domains with less computation overhead.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2009-2019
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• 2013

In this paper, we propose an automatic modulation recognition algorithm based on cyclic moment and new modified cumulant for analog and digital modulation signals. It is noteworthy that each modulated signal has different cycle frequency characteristics according to its order of cyclic moment. By means of this characteristics as classification features, various modulated signals can be efficiently classified. Also, to identify modulated signals having the same cycle frequency characteristics, we take advantage of the additional classification factors such as variations of envelope and phase as well as modified cumulant. The proposed algorithm was evaluated by considering the number of symbols, SNR, and frequency offset. In the simulation condition where the number of gathered symbols was about 819, and SNR and frequency offset were above 10dB and below 25%, respectively, the average accuracy of the proposed algorithm was more than 95%.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.195-204
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• 2014

In this paper, we propose an automatic modulation classification method for ten digitally modulated baseband signals, such as 2-FSK, 4-FSK, 8-FSK, MSK, BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, and 64-QAM based on higher order statistics of cyclostationary process. The first order cyclic moments and higher order cyclic cumulants of the signal are used as features of the modulation signals. The proposed method consists of two stages. At the first stage, we classify modulation signals as M-FSK and non-FSK using peaks of the first order cyclic moment. At the next step, we apply the Gaussian mixture model-based classifier to classify non-FSK. Simulation results are demonstrated to evaluate the proposed scheme. The results show high probability of classification even in the presence of frequency and phase offsets.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.875-878
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• 2001

본 논문에서는 디지털 영상의 변조를 확인함과 동시에 변조의 위치를 확인하는 연성 워터마킹(fragile watermarking)을 위하여 DCT를 통해 블록의 에너지를 계산한 후, 에너지의 단계별로 워터마크의 삽입량을 조절하는 방법을 제안한다. 디지털 영상의 소유권 확인을 위해 디지털 서명을 사용하며, 영상에 DCT를 수행함으로서 모든 픽셀에 워터마크를 삽입하지 않고서도 변조의 유무를 확인하는 것이 가능하 방식이다. DCT 계수로 각 블록의 에너지를 계산하여 블록의 관계를 분류하며 에너지가 작은 블록들과 큰 블록들은 이간의 시각에 민감한 부분이므로 워터마크의 삽입 양을 줄이고, 중간 단계의 블록일수록 워터마크의 삽입 양을 늘린다. 에너지의 단계 분류에 의해 가변적으로 워터마크를 삽입함으로 워터마크의 비가시성과 연성을 만족시키며 변조의 유무와 위치를 확인할 수 있게 된다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1275-1281
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• 2019

The automatic modulation recognition of a radio signal is a major task of an intelligent receiver, with various civilian and military applications. In this paper, we propose a method to recognize the modulation of radio signals in wireless communication based on the deep neural network. We classify the modulation pattern of radio signal by using the LSTM model, which can catch the long-term pattern for the sequential data as the input data of the deep neural network. The amplitude and phase of the modulated signal, the in-phase carrier, and the quadrature-phase carrier are used as input data in the LSTM model. In order to verify the performance of the proposed learning method, we use a large dataset for training and test, including the ten types of modulation signal under various signal-to-noise ratios.