Automatic digital modulation recognition using artificial neural network and genetic algorithm

Wong, M. L. Dennis; Nandi, Asoke K.

doi:10.1016/j.sigpro.2003.10.019

Cited by 203 publications

(114 citation statements)

References 18 publications

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“…In this paper, we estimate the instantaneous frequency and instantaneous phase from samples. The standard deviation of instantaneous phase is addressed in [9]. For brevity,…”

Section: Based On the Instantaneous Propertiesmentioning

confidence: 99%

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Neural Networks for Radar Waveform Recognition

et al. 2017

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For passive radar detection system, radar waveform recognition is an important research area. In this paper, we explore an automatic radar waveform recognition system to detect, track and locate the low probability of intercept (LPI) radars. The system can classify (but not identify) 12 kinds of signals, including binary phase shift keying (BPSK) (barker codes modulated), linear frequency modulation (LFM), Costas codes, Frank code, P1-P4 codesand T1-T4 codeswith a low signal-to-noise ratio (SNR). It is one of the most extensive classification systems in the open articles. A hybrid classifier is proposed, which includes two relatively independent subsidiary networks, convolutional neural network (CNN) and Elman neural network (ENN). We determine the parameters of the architecture to make networks more effectively. Specifically, we focus on how the networks are designed, what the best set of features for classification is and what the best classified strategy is. Especially, we propose several key features for the classifier based on Choi-Williams time-frequency distribution (CWD). Finally, the recognition system is simulated by experimental data. The experiments show the overall successful recognition ratio of 94.5% at an SNR of −2 dB.

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“…In this paper, we estimate the instantaneous frequency and instantaneous phase from samples. The standard deviation of instantaneous phase is addressed in [9]. For brevity,…”

Section: Based On the Instantaneous Propertiesmentioning

confidence: 99%

“…The artificial neural network is also utilized in the recognition system. The multi-layer perceptron (MLP) recognizer reaches more than 99% recognized performance at SNR ≥0 dB [9]. Atomic decomposition (AD) is also addressed in the detection and classification of complex radar signals.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Radar Waveform Recognition

et al. 2017

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“…On this basis, literature [19] used ANN neural network for modulation classification. However, the ANN network was only limited to the shallow network.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Modulation Classification Based on Extensible Neural Networks

Yang

2017

Mathematical Problems in Engineering

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A deep learning architecture based on Extensible Neural Networks is proposed for modulation classification in multipath fading channel. Expanded Neural Networks (ENN) are established based on energy natural logarithm model. The model is set up using hidden layers. Modulation classification based on ENN is implemented through the amplitude, phase, and frequency hidden network, respectively. In order to improve Probability of Correct classification (PCC), one or more communication signal features are extracted using hidden networks. Through theoretical proof, ENN learning network is demonstrated to be effective in improving PCC using amplitude, phase, and the frequency weight subnetwork, respectively. Compared with the traditional algorithms, the simulation results show that the proposed ENN has higher PCC than traditional algorithm for modulation classification within the same training sequence and Signal to Noise Ratio (SNR).

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“…Many of these structures are based on V-BLAST technique. In [11] authors propose a receiver side vertical Bell Laboratories layered Space time (V-BLAST) MIMO processing communication architecture where the transmitter side jointly encoding is indispensable. Using VBLAST for spatial multiplexing, the authors in [12] optimized MIMO's operation under power and delay constraints.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-efficient Cooperative Transmission Scheme Based on V-BLAST Processing and ICA clustering for WSNs Lifetime Maximization

Kerdabadi

Ghazizadeh²

2015

AJST

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In wireless sensor network, one of the main objectives is how to improve its lifetime because the sensor nodes are generally energy-limited. Thus, providing ways to optimize energy Consumption, which ultimately increases the network lifetime, is of great importance. In this paper, we propose and evaluate an energy-efficient cooperative MIMO transmission scheme based on V-BLAST technique. In proposed scheme the clustering is done based on Imperialist Competitive Algorithm (ICA) then V-BLAST technique based virtual MIMO transmission are used. This proposed protocol performance is analytically and experimentally is compared with previous presented works. From the analysis and the simulation results, it is seen that the proposed method can reduce the energy consumption of the network and prolong the sensor networks lifetime compared with existing techniques.

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Automatic digital modulation recognition using artificial neural network and genetic algorithm

Cited by 203 publications

References 18 publications

Neural Networks for Radar Waveform Recognition

Neural Networks for Radar Waveform Recognition

Modulation Classification Based on Extensible Neural Networks

A Novel Energy-efficient Cooperative Transmission Scheme Based on V-BLAST Processing and ICA clustering for WSNs Lifetime Maximization

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