Automatic Modulation Recognition using Support Vector Machine in Software Radio Applications

Park, Cheol-Sun; Jang, Won; Nah, Sun-Phil; Kim, Dae Young

doi:10.1109/icact.2007.358249

Cited by 30 publications

(8 citation statements)

References 3 publications

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“…On one hand, different kinds of features are utilized for modulation classification, such as statistical features [10], high-order cumulants [11], [12] and wavelet cyclic features [13]. On the other hand, some well-known classifiers are employed in the AMC system, including support vector machine (SVM) [14], neural networks (NN) [15], k-nearest neighbors (KNN) [16], and decision tree [17]. With any strategy adopted, the design of these methods typically rely on handcrafted feature engineering which is determined based on the specific case and professional domain knowledge.…”

Section: A Automatic Modulation Classificationmentioning

confidence: 99%

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

et al. 2019

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With the recent advances in wireless technologies, high frequency radio has become the primary medium for long-distance communication. Among various types of modulation in signal transmission, morse code stands out due to its simplicity and efficiency in information transmission while costing small bandwidth. In practice, however, it is extremely laborious to locate morse signals in wideband communication. It's a needle in a haystack, if morse code is sent in random carrier waves at random period. To avoid this, automatic morse signal detection has become a challenging task in wireless morse communications, and new solutions could be derived from the latest machine learning techniques. In this paper, we propose a deep learning framework, namely DeepMorse, to blindly detect morse signals in wideband spectrum data. In particular, we first develop a multi-signal sensing module to retrieve signal candidates from wideband spectrum without prior knowledge. Then, we construct a CNN-based module to extract informative features from the located candidates, in order to distinguish the morse signal from other types of modulation. To evaluate the proposed DeepMorse model, we set up a testbed utilizing commercialized long-distance wireless communication devices. The experimental results demonstrate that DeepMorse is able to effectively detect morse signals and outperform the state-of-the-art methods on four real-world datasets. INDEX TERMS Deep learning, blind signal detection, morse code, wideband wireless spectrum.

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Section: A Automatic Modulation Classificationmentioning

confidence: 99%

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

et al. 2019

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“…Since SVM is basically a binary classifier, WET it is not straightforward to apply it to multi-class classification problems. The most typical method for the multiclass problem is to classify one class from the other classes (refer 1-v-r), another typical method is to combine all possible two-class (pair wise) classifiers (refer 1-v-I) [10]. It's known as 1-v-I type SVM is superior to 1-v-r with respect to its learning time, but execution time for classification of 1-v-I is much worse than 1-v-r [16].…”

Section: Svmmentioning

confidence: 99%

“…In [10], a total of 7 statistical signal features were extracted and used to classify 9 modulation signals. The authors investigated the performance of the two types of SVM classifiers.…”

Section: Introductionmentioning

confidence: 99%

Blind Modulation Recognition in Wireless MC-CDMA Systems Using a Support Vector Machine Classifier

Keshk¹,

El-Rabie²,

El‐Samie³

et al. 2013

WET

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Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Multi-Carrier Code Division Multiple Access (MC-CDMA) systems using Discrete Transforms (DTs) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. This algorithm uses various DT techniques such as the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract features from the modulated signal and a Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in Artificial Neural Networks (ANNs). Simulation results shows the classifier is capable of recognizing the modulation scheme with high accuracy up to 90% - 100% using DWT, DCT and DST for some modulation schemes over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low Signal-to-Noise ratios (SNRs).

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“…In recent years, as machine learning (ML) techniques continually demonstrate significant breakthroughs in various fields, many researchers also exploit ML techniques as classifiers with extracted features, such as support vector machine, K-nearest neighbor, and genetic programming [24]- [26]. Without extracting hand-engineered features, deep learning (DL) can automatically learn the high-level features.…”

Section: Introductionmentioning

confidence: 99%

Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification With Channel Compensation Mechanism

Chen

Chou

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, conventional DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Vehicle-to-Everything (V2X) applications. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach nearoptimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness. The lightweight and efficient learning features of the proposed mechanism will be very attractive for future resourceconstrained/aware IoT/V2X applications.

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Automatic Modulation Recognition using Support Vector Machine in Software Radio Applications

Cited by 30 publications

References 3 publications

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

Blind Modulation Recognition in Wireless MC-CDMA Systems Using a Support Vector Machine Classifier

Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification With Channel Compensation Mechanism

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