Publicado:
2023-09-26Número:
Vol. 17 Núm. 2 (2023)Sección:
Visión InvestigadoraDiseño e implementación de un Sistema de clasificación de modulaciones digitales usando algoritmos inteligentes
Design and implementation of a digital modulation classification system using intelligent algorithms
Palabras clave:
Error vector magnitude, Signal to noise ratio, Cognitive radio, Constellation diagram, Digital modulation, Convolutional neural networks, Dataset (en).Palabras clave:
Magnitud del vector de error, Relación señal a ruido, Radio cognitiva, Diagrama de constelación, Modulación digital, Redes neuronales convolucionales, Dataset (es).Descargas
Resumen (es)
Las redes neuronales presentan una gran variedad de aplicaciones, entre ellas se encuentra la clasificación de modulaciones para aplicación en radio cognitivo. Esta área ha sido estudiada a lo largo de los años porque el trabajo conjunto de ambas tecnologías facilita la administración y optimización del espectro radioeléctrico. En esta investigación se propone un dataset compuesto de seis tipos de modulaciones digitales diferentes obtenidas mediante software para el entrenamiento de redes neuronales en búsqueda de la clasificación para la aplicación en radio cognitivo, evaluando un modelo de red neuronal entrenado y probado con el dataset creado en esta investigación, mediante métricas de porcentaje de error y precisión.
Resumen (en)
Neural networks present a variety of applications, among them is the classification of modulations for application un cognitive radio. This area has been studied over the years because the work of both technologies facilitates the administration and optimization of the radio spectrum. In this research, a dataset composed of 6 different types of digital modulations obtained by software for training a proposed neural network in search of the classification of signals for application in cognitive radio networks. The model is evaluated by being trained and tested with the dataset created in this study, analyzing metrics such as percentage error and precision.
Referencias
A. M. Wyglinski, M. Nekovee, and Y. T. Hou, Cognitive Radio Communications and Networks: Principles and Practice. 2009.
J. Mitola and G. Q. Maguire, "Cognitive radio: making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, 1999. https://doi.org/10.1109/98.788210
J. H. Aguilar Rentería and A. Navarro Cadavid, "Cognitive radio - State of the Art," Sist. y Telemática, vol. 9, no. 16, p. 31, 2011. https://doi.org/10.18046/syt.v9i16.1028
G. Arulampalam, V. Ramakonar, A. Bouzerdoum, and D. Habibi, "Classification of digital modulation schemes using neural networks," ISSPA 1999 - Proc. 5th Int. Symp. Signal Process. Its Appl., vol. 2, pp. 649-652, 1999. https://doi.org/10.1109/ISSPA.1999.815756
A. K. Nandi and E. E. Azzouz, "Modulation recognition using artificial neural networks," Signal Processing, vol. 56, no. 2, pp. 165-175, 1997. https://doi.org/10.1016/S0165-1684(96)00165-X
O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, "Survey of automatic modulation classification techniques: Classical approaches and new trends," IET Commun., vol. 1, no. 2, pp. 137-156, 2007. https://doi.org/10.1049/iet-com:20050176
A. Ali, F. Yangyu, and S. Liu, "Automatic modulation classification of digital modulation signals with stacked autoencoders," Digit. Signal Process. A Rev. J., vol. 71, pp. 108-116, 2017. https://doi.org/10.1016/j.dsp.2017.09.005
Y. Tevfik and A. Huseyin, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE Commun. Surv. Tutorials, vol. 11, no. 1, pp. 116-130, 2009. https://doi.org/10.1109/SURV.2009.090109
T. J. O'Shea, J. Corgan, and T. C. Clancy, "Convolutional radio modulation recognition networks," Commun. Comput. Inf. Sci., vol. 629, pp. 213-226, 2016. https://doi.org/10.1007/978-3-319-44188-7_16
S. Ramjee, S. Ju, D. Yang, X. Liu, A. El Gamal, and Y. C. Eldar, "Fast Deep Learning for Automatic Modulation Classification," no. 108818, 2019. http://arxiv.org/abs/1901.05850
T. J. O'Shea, T. Roy, and T. C. Clancy, "Over-the-Air Deep Learning Based Radio Signal Classification," IEEE J. Sel. Top. Signal Process., vol. 12, no. 1, pp. 168-179, 2018. https://doi.org/10.1109/JSTSP.2018.2797022
T. Huynh-The, C. H. Hua, J. W. Kim, S. H. Kim, and D. S. Kim, "Exploiting a low-cost CNN with skip connection for robust automatic modulation classification," IEEE Wirel. Commun. Netw. Conf. WCNC, vol. 2020-May, 2020. https://doi.org/10.1109/WCNC45663.2020.9120667
S. H. Kim, J. W. Kim, W. P. Nwadiugwu, and D. S. Kim, "Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks," IEEE Access, vol. 9, pp. 92386-92393, 2021. https://doi.org/10.1109/ACCESS.2021.3091421
P. Ghasemzadeh, S. Banerjee, M. Hempel, and H. Sharif, "A Novel Deep Learning and Polar Transformation Framework for an Adaptive Automatic Modulation Classification," IEEE Trans. Veh. Technol., vol. 69, no. 11, pp. 13243-13258, 2020. https://doi.org/10.1109/TVT.2020.3022394
S. Peng et al., "Modulation Classification Based on Signal Constellation Diagrams and Deep Learning," IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 3, pp. 718-727, 2019. https://doi.org/10.1109/TNNLS.2018.2850703
K. Jiang, J. Zhang, H. Wu, and A. Wang, "applied sciences Based on Deep Convolutional Neural Network," pp. 1-14, 2020.
N. Daldal, Z. Cömert, and K. Polat, "Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time-frequency information," Appl. Soft Comput. J., vol. 86, , p. 105834, 2020. https://doi.org/10.1016/j.asoc.2019.105834
W. Wang, "A Brief Survey on Cognitive Radio," in Cognitive Radio Systems, China: InTech, 2009. https://doi.org/10.5772/7842
Y. Arjoune and N. Kaabouch, "A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions," Sensors (Switzerland), vol. 19, no. 1, Jan. 2019. https://doi.org/10.3390/s19010126
S. Haykin and P. Setoodeh, "Cognitive Radio Networks: The Spectrum Supply Chain Paradigm," IEEE Trans. Cogn. Commun. Netw., vol. 1, no. 1, pp. 3-28, 2015. https://doi.org/10.1109/TCCN.2015.2488627
R. G. Nair and K. Narayanan, "Cooperative spectrum sensing in cognitive radio networks using machine learning techniques," Appl. Nanosci., vol. 31, no. 11, pp. 2209-2221, 2022. https://doi.org/10.1007/s13204-021-02261-0
GNU Radio, "About GNU Radio · GNU Radio." https://www.gnuradio.org/about/
Mathworks, "What Is MATLAB? - MATLAB & Simulink." https://www.mathworks.com/discovery/what-is- matlab.html
C. Ballesteros, E. P. Estupinan Cuesta, and J. C. Martinez Quintero, "Digital Modulation Constellation Images," vol. 1, 2022. https://doi.org/10.17632/WG2GN8D5G9.1
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, inception-ResNet and the impact of residual connections on learning," 31st AAAI Conf. Artif. Intell. AAAI 2017, pp. 4278-4284, 2017. https://doi.org/10.1609/aaai.v31i1.11231
A. Alemi, "Improving Inception and Image Classification in TensorFlow - Google AI Blog," 2016. https://ai.googleblog.com/2016/08/improving-inception-and-image.html
Tensorflow, "tf.keras.losses.SparseCategoricalCrossentropy | TensorFlow Core v2.9.1." https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy
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