Modulation classification using deep learning technique

Z. Zhu, A.K. Nandi, Automatic Modulation Classification: Principles, Algorithms and Applications, Automatic Modulation Classification: Principles, Algorithms and Applications, 1-163 (2015)

C. Zhang, P. Patras, H. Haddadi, Deep learning in mobile and wireless networking: A survey’’. in IEEE Commun. Surv. Tutorials, 21(3), 2224_2287, 3rd Quart., (2019)

Y.A. Eldemerdash, O.A. Dobre, M. Oner, Signal identification for multiple-antenna wireless systems: Achievements and challenges’’. in IEEE Commun. Surv. Tutorials, 18(3), 1524_1551, 3rd Quart., (2016)

T. Huynh-The et al., Automatic Modulation Classification: A Deep Architecture Survey, in IEEE Access, vol. 9, pp. 142950–142971, (2021)

T. Xu, Y. Ma, Signal Automatic Modulation Classification and Recognition in View of Deep Learning. IEEE Access. 11, 114623–114637 (2023)

Article  Google Scholar 

F. Meng, P. Chen, L. Wu, X. Wang, Automatic modulation classification: A deep learning enabled approach’’, in IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 10760_10772, Nov. (2018)

S. Chen, Y. Zhang, Z. He, J. Nie, W. Zhang, A novel attention cooperative framework for automatic modulation recognition’’, IEEE Access vol. 8 (pp. 15673_15686, 2020)

I. Rodomagoulakis, P. Maragos, On the improvement of modulation features using multi-microphone energy tracking for robust distant speech recognition, 2017 25th European Signal Processing Conf. (EUSIPCO), Kos, Greece, pp. 558–562, (2017)

N.G.V. Vijayan, R. Jose, Performance Analysis of Modulation Classification Using Machine learning, 2021 8th International Conference on Smart Computing and, Communications, (ICSCC), Kochi, Kerala, India, pp. 70–74, (2021)

A.K. Nandi, E.E. Azzouz, Algorithms for automatic modulation recognition of communication signals. IEEE Trans. Commun. 46(4), 431–436 (1998)

Article  Google Scholar 

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y.-D. Yao, Modulation classification using convolutional Neural Network based deep learning model, 2017 26th Wireless and Optical Communication Conference (WOCC), Newark, NJ, USA, pp. 1–5, (2017)

T.J. O’Shea, N. West, ‘Radio machine learning dataset generation with GNU radio’. Proc. of the GNU Radio Conf., Boulder, CO, USA, 1 (1), (2016)

H. Elyousseph, M.L. Altamimi, Deep Learning Radio Frequency Signal Classification with Hybrid Images, 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Terengganu, Malaysia, pp. 7–11, (2021)

N.E. West, T. O’Shea, Deep architectures for modulation recognition, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, USA, pp. 1–6, (2017)

X. Liu, D. Yang, A.E. Gamal, Deep neural network architectures for modulation classification, 2017 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, pp. 915–919, (2017)

T.J. O’Shea, J. Corgan, T.C. Clancy, Convolutional Radio Modulation Recognition Networks, in Engineering Applications of Neural Networks, EANN 2016, Communications in Computer and Information Science vol. 629, ed. by C. Jayne, L. Iliadis (Springer, Cham, 2016), pp. 213–226

Chapter  Google Scholar 

H. Dai, Y.K. Chembo, Classification of IQ-Modulated Signals Based on Reservoir Computing With Narrowband Optoelectronic Oscillators, in IEEE Journal of Quantum Electronics, 57(3), 1–8, June (2021)

T.J. O’Shea, T. Roy, T.C. Clancy, Over-the-air Deep Learning Based Radio Signal classification. IEEE J. Selec. Topics Signal Process. 12(1), 168–179 (2018)

Article  ADS  Google Scholar 

J. Jagannath et al., Artificial Neural Network Based Automatic Modulation Classification over a Software Defined Radio Testbed, 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, pp. 1–6 (2018)

Y. Wang, M. Liu, J. Yang, G. Gui, Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios, in IEEE Transactions on Vehicular Technology, 68(4), 4074–4077, April (2019)

N.B. Karayiannis, Reformulated radial basis neural networks trained by gradient descent, in IEEE Transactions on Neural Networks, 10(3), 657–671, May (1999)

D. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, International Conference on Learning Representations, (2014)

M.C. Mukkamala, M. Hein, Variants of RMSProp and Adagrad with logarithmic regret bounds, pp. 2545–2553 (2017)

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