Automatic Modulation Classification in Cognitive Radio Using Multiple Antennas and Maximum-Likelihood Techniques

Salam, Ahmed O. Abdul; Sheriff, Ray E.; Al-Araji, Saleh R.; Mezher, Kahtan; Nasir, Qassim

doi:10.1109/cit/iucc/dasc/picom.2015.3

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…The authors used likelihood-based statistical tests to show that the maximum likelihood optimum modulation classification using matched filters or correlators outperforms the classical Newmann-Pearson minimum distance classifier. In [14], the automatic modulation classification is linked to the accurate identification of a received signal modulation aiming to explore the opportunities of the AMC detection accuracy improvements using MIMO and diversity combining settings.…”

Section: Related Workmentioning

confidence: 99%

“…We can now reformulate the secondary transmission capacity expression (10) by using (13) and (14), resulting in…”

Section: Statement Of the Maximisation Problemmentioning

confidence: 99%

“…(20) Proof: See Appendix 1. □ Using (13) and (14), the optimum secondary transmit power and density of users are given, respectively, by P s, m…”

Section: Maximum Secondary Capacity For a Given Modulation Ordermentioning

confidence: 99%

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Optimisation of the transmission capacity of cognitive networks

Pimentel

Cardieri

2020

IET Communications

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Section: Related Workmentioning

confidence: 99%

“…We can now reformulate the secondary transmission capacity expression (10) by using (13) and (14), resulting in…”

Section: Statement Of the Maximisation Problemmentioning

confidence: 99%

See 1 more Smart Citation

Optimisation of the transmission capacity of cognitive networks

Pimentel

Cardieri

2020

IET Communications

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“…There are two categories of traditional AMC algorithms, the method based on likelihood estimation [5] [6] and the handcraft feature extraction with expert experience [7] [8]. In the likelihood estimation method, the modulation classification problem is represented as a multiple hypothesis testing problem.…”

Section: Introductionmentioning

confidence: 99%

Dual-Channel Hybrid Neural Network for Modulation Recognition

et al. 2021

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In the blind recognition of wireless communication modulation based on deep learning, how to further improve the recognition accuracy has become a key research issue. In this work, we propose a dual-channel hybrid model termed as CLDR (convolutional long short-term deep neural and residual network). The CLDR consists of the convolutional long short-term deep neural network (CLDNN) and the residual network (ResNet), where CLDNN is to reduce the variations in the spectrum and time, ResNet is to avoid gradient vanishing or exploding. In addition, we design an exponential curve decay adaptive cyclical learning rate method to decrease the training time cost of the neural network model. This method eliminates the need to experimentally search the optimal learning rate as with the fixed learning rate policy. It also avoids the slow convergence of the model due to the excessive attenuation amplitude as with the triangular learning rate policy. We test the feasibility of the CLDR model and discuss the influence of the exponential decay cyclical learning rate on the training of CLDR model based on the RadioML2016.10b public dataset. Simulation results show that the CLDR model yields a recognition accuracy of 93.1% at high SNRs, which effectively reduces the influence of external environment such as noise and fading on recognition accuracy. The training time cost of CLDR using exponential cyclical learning rate is reduced by 14.6% and 32.1% compared with triangular and fixed methods. Therefore, the exponential cyclical learning rate policy effectively reduces the training time cost of the model in the same classification accuracy. INDEX TERMSDeep neural network, Residual network, Cyclical learning rate, Modulation recognition.

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“…Even if the antenna is able to capture a satellite signal, it will not know with certainty its source, i.e., the satellite that generated it. As a result, the ground station does not have the required information to decode the signal [13].…”

Section: Introductionmentioning

confidence: 99%

Signal Detection and Modulation Classification for Satellite Communications

Toro-Betancur

Valencia

Bernal

2020

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning

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Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolutevalued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks. CCS CONCEPTS• Computing methodologies → Supervised learning; • Applied computing → Aerospace.

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Automatic Modulation Classification in Cognitive Radio Using Multiple Antennas and Maximum-Likelihood Techniques

Cited by 9 publications

References 19 publications

Optimisation of the transmission capacity of cognitive networks

Optimisation of the transmission capacity of cognitive networks

Dual-Channel Hybrid Neural Network for Modulation Recognition

Signal Detection and Modulation Classification for Satellite Communications

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