Automatic modulation format/bit-rate classification and signal-to-noise ratio estimation using asynchronous delay-tap sampling

Khan, Faisal Nadeem; Teow, Chiew Hoon; Kiu, Shiu Guong; Tan, Ming; Zhou, Yudi; Al-Arashi, Waled Hussein; Lau, Alan Pak Tao; Lu, Chao

doi:10.1016/j.compeleceng.2015.09.005

Cited by 24 publications

(14 citation statements)

References 33 publications

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“…In the fourth simulation, we used an automatic digital modulation classification method to evaluate the security of the communication scheme. At present, many methods [60]- [62] have been proposed to automatically classify the digital modulation manners of communication information, such as 2-ary amplitude shift keying(2ASK), 2-ary frequency shift keying(2FSK), 2-ary phase shift keying(2PSK), 16-ary quadrature amplitude modulation (16QAM) and so on. Moreover, these classification methods are usually used by eavesdroppers of the adversary.…”

Section: Discussionmentioning

confidence: 99%

A Basic Bio-Inspired Camouflage Communication Frame Design and Applications for Secure Underwater Communication Among Military Underwater Platforms

et al. 2020

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Many marine mammal species, such as different kinds of whales and dolphins, live together in groups. Although having no nice eyesight, they can accomplish cooperative foraging, inter-communication and group sailing very efficiently and accurately only depending on their sound. Generally, present interception systems almost always classify biological signals as ocean noise and try to filter them out. In addition, the covertness and security are very important for many military underwater platforms (MUPs) and their formations. Based on above classification fact and covert communication demand, this article designs a basic bio-inspired camouflage communication frame (BBICCF) for secure underwater communication among military underwater platforms based on the killer whale sound. According to characteristics of the killer whales' original call pulse trains, the original long duration call pulses (whistles and pulsed calls) are utilized as communication address codes for each MUP so as to provide disguised communication addressing support for interconnection among multiple MUPs in the same formation, and the original short duration call pulses (clicks) are used as other communication codes, so as to construct BBICCF for highly camouflaged conveying communication information. A simple and effective time-frequency (TF) contour extraction method is proposed to achieve the accurate extraction of the TF contour of the fundamental frequency of whistles and pulsed calls of killer whales for efficient classification and decoding of address codes. Next, this article provides some extensions and applications about how to let the BBICCF to be applied in conventional communication networks for MUP formation. Finally, simulation results show the effectiveness and concealment ability of designed BBICCF. INDEX TERMS Secure communication network, underwater communications, whale sound, underwater formation. I. INTRODUCTION With the development of technology, more and more autonomous underwater vehicle (AUV), glider and varieties of formations composed of them are used in many military application scenarios from surveillance and anti-submarine, The associate editor coordinating the review of this manuscript and approving it for publication was Xingwang Li. to reconnaissance and mine detection etc. [1]-[3]. Compared with single MUP, the formation composed of multiple MUPs can accomplish more complex, wider range, and more reliable military tasks [4], [5], just as multitudinous group animals in nature, such as whales, swarms, wolf packs etc.. However, the accomplishment of collaborative tasks among the MUPs, such as cooperative localization, sailing of multiple platforms, interaction of the order/data among multiple

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Section: Discussionmentioning

confidence: 99%

A Basic Bio-Inspired Camouflage Communication Frame Design and Applications for Secure Underwater Communication Among Military Underwater Platforms

et al. 2020

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“…Hidden layers maybe one layer or multilayer, and each layer consists of several nodes. The [26,37] (ii) KNN [38,91] (iii) SVM [6,27,47,48,92] (iv) Naïve Bayes [39] (v) HMM [46] (vi) Fuzzy classifier [93] (vii) Polynomial classifier [40,94] (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 73-76, 79, 81, 82, 95, 96] (iv) LSTM [29,69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (ix) NFSC [78] Pros (i) works better on small data (ii) low implementation cost (i) simple pre-processing (ii) high accuracy and efficiency (iii) adaptive to different applications Cons (i) time demanding (ii) complex feature engineering (iii) depends heavily on the representation of the data (iv) prone to curse of dimensionality (i) demanding large amounts of data (ii) high hardware cost node presented in Figure 3 is the basic operational unit, in which the input vector is multiplied by a series of weights and the sum value is fed into the activation function . These operational units contribute to a powerful network, which could realize complex functions such as regression and classification.…”

Section: Definition Of DL Problemmentioning

confidence: 99%

“…The flexibility of the proposed model was also validated in scenarios with variable symbol rates. Unlike the existing methods which only focus on modulation types classification, in [30,31], they propose a novel scheme for classifying modulation format and estimating SNR simultaneously. The asynchronous delay-tap plots are extracted as the training data and two multilayer perceptron (MLP) architectures are adopted for real-world signal recognition tasks.…”

Section: Instantaneous Time Features Generally Instantaneousmentioning

confidence: 99%

“…Wireless technology recognition Algorithms (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 95] (iv) LSTM [69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (i) CNN [73-76, 79, 81, 82, 96] (ii) LSTM [29] (iii) NFSC [78] [74], the amplitude and phase difference representation were employed for CNN training procedure. The results indicate that the recognition of radar signals has been realized successfully with the proposed scheme even under the condition of LTE and WLAN signals coexisting at the same time.…”

Section: Modulation Recognitionmentioning

confidence: 99%

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A Survey on Deep Learning Techniques in Wireless Signal Recognition

Dong

Zhou

et al. 2019

Wireless Communications and Mobile Computing

109

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Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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“…The neural network [13] is a fascinating classification method with a series of state-of-the-art achievements automatic modulation classification [14,15]. For instance, O'Shea et al [16] trained a deep neural network (DNN) using a baseband IQ waveform to identify modulation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Digital Modulation Classification Based on Curriculum Learning

Zhang

Wang

et al. 2019

Applied Sciences

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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.

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Automatic modulation format/bit-rate classification and signal-to-noise ratio estimation using asynchronous delay-tap sampling

Cited by 24 publications

References 33 publications

A Basic Bio-Inspired Camouflage Communication Frame Design and Applications for Secure Underwater Communication Among Military Underwater Platforms

A Basic Bio-Inspired Camouflage Communication Frame Design and Applications for Secure Underwater Communication Among Military Underwater Platforms

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Automatic Digital Modulation Classification Based on Curriculum Learning

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