Metodología de estimación automática multiportadora de respuesta de canal de transmisión de datos

dc.contributor.advisorGuerrero-Gonzalez, Neil
dc.contributor.authorCortés Cortés, Claudia Lucía
dc.contributor.cvlacCortés Cortés, Claudia Lucía [0001381627]spa
dc.contributor.orcidCortés Cortés, Claudia Lucía [0000-0001-5760-9990]spa
dc.contributor.researchgroupGtt ­ Grupo de Investigación en Telemática y Telecomunicacionesspa
dc.date.accessioned2023-01-19T19:02:49Z
dc.date.available2023-01-19T19:02:49Z
dc.date.issued2022
dc.descriptiongraficas, tablasspa
dc.description.abstractEn el marco de los Objetivos de Desarrollo Sostenible (ODS) y la Agenda 2030 acordada por los países reunidos en las Naciones Unidas en el año 2015, entre ellos Colombia, se hace necesaria la innovación tecnológica en las Tecnologías de la Información y Comunicaciones como eje transversal para el cumplimiento de los ODS. En esta tesis se propone una metodología para la caracterización automática de la respuesta de fase del canal de transmisión de comunicaciones como contribución científica al estado del arte. La metodología propuesta es escalable a cualquier canal de transmisión dado que se basa en el procesamiento de imágenes de diagramas de constelación con redes neuronales convolucionales, imágenes que son generadas a partir de una señal 4-QAM (QAM, Quadrature-Amplitude Modulation) modificada. La metodología propuesta para la estimación de la respuesta de fase divide la banda de frecuencia disponible en sub-bandas y usa técnicas de modulación y multiplexación avanzadas que permiten obtener el desplazamiento de fase por sub-banda y realizar la compensación de este desplazamiento para demodular la señal de información adaptandose a las variaciones rápidas del canal de transmisión. El uso de técnicas de modulación y multiplexación capaces de operar para grandes tasas de transmisión con suficiente robustez respecto a las características de ruido del canal de comunicaciones se convierten en parte fundamental para el avance de los sistemas de comunicaciones, donde la Multiplexación por División de Frecuencias Ortogonales (OFDM, Orthogonal Frequency Division Multiplexing) es la técnica de modulación de mayor difusión. Esta técnica divide el espectro disponible en múltiples subportadoras utilizándolo de forma más eficiente. El uso de OFDM como esquema multiportadora permite transmitir la información por N subcanales, de forma que el sistema se podría ver como N sistemas de portadora única con respuesta en frecuencia plana, razón por la cual este esquema multiportadora no es capaz de adaptarse a las variaciones rápidas del canal de transmisión. (Texto tomado de la fuente)spa
dc.description.abstractSustainable Development Goals (SDG) framework and 2030 Agenda agreed at the United Nations in 2015 countries meeting, including Colombia, Technological innovation in Information and Communication Technologies is necessary as a transversal axis for SDGs fulfillment. In this thesis is proposed a methodology for communications transmission channel phase response automatic’s characterization, as a scientific contribution to the state of the art. The proposed methodology is scalable to any transmission channel since it is based on constellation diagram image processing with convolutional neural networks, images from modified 4-QAM signals (QAM, Quadrature-Amplitude Modulation). The proposed methodology for phase response estimation divides the available frequency band in sub-bands and uses advanced modulation and multiplexing techniques that allow to obtain the sub-band phase shift and to compensate the phase offset per sub-band in order to demodulate the information signal by adapting to rapid variations of the transmission channel. The use of modulation and multiplexing techniques capable of operating for large transmission rates regarding the noise characteristics of the communications channel become a fundamental part for communications systems advancement, where Orthogonal Frequency Division Multiplexing (OFDM) is the most widespread modulation technique. OFDM divides the available spectrum into multiple subcarriers, using it more efficiently. OFDM, as a multicarrier modulation scheme, allows information to be transmitted by N subchannels, so that the system could be seen as N single-carrier systems with flat frequency response, which is why this multicarrier scheme is not able to adapt to transmission channel rapid variations.eng
dc.description.curricularareaEléctrica, Electrónica, Automatización Y Telecomunicacionesspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.format.extentxviii, 98 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/83030
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automáticaspa
dc.relation.referencesMicrosoft, “WORLDWIDE UTILITIES INDUSTRY SURVEY 2010,” Tech. Rep. March, 2010.spa
dc.relation.referencesP. J. Winzer, “Modulation and multiplexing in optical communications,” 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference, no. February, pp. 3–4, 2009.spa
dc.relation.referencesS. Hara and R. Prasad, Multicarrier techniques for 4G Mobile Communications. Artech House, Inc.685 Canton St. Norwood, MA, United States, 2003.spa
dc.relation.referencesUniversidad Nacional Autónoma de México, “Sistema de Comunicaciones Óptico: Estado del Arte,” 2011.spa
dc.relation.referencesC. A. Balbuena-Campuzano and F. J. García-Ugalde, “Rendimiento de un sistema de control de errores con turbo códigos para canales PLC,” Ingeniería Investigación y Tecnología, vol. 15, no. 3, pp. 363–376, 2014.spa
dc.relation.referencesNaciones Unidas, Agenda 2030 y los objetivos de desarrollo sostenible: una oportunidad para América Latina y el Caribe. 2018.spa
dc.relation.referencesNaciones Unidas, “Transformar nuestro mundo: la Agenda 2030 para el Desarrollo Sostenible,” 2015.spa
dc.relation.referencesProspectiva 2020 Foresight, “MATRIZ ENERGÉTICA MUNDIAL (Parte III),” 2013.spa
dc.relation.referencesUnited Nations Educational Scientific and Cultural Organization – UNESCO, “Agua y Energía: Datos y Estadísticas,” 2014.spa
dc.relation.referencesA. G. Peralta Sevilla and F. Amata Fernández, “Evolución de las Redes Eléctricas hacia Smart Grid en Países de la Región Andina,” Revista Educación en Ingeniería, vol. 8, pp. 48–61, jun. 2013.spa
dc.relation.referencesM. MINETAD, “Smart Grids y la Evolución de la Red Eléctrica,” 2011.spa
dc.relation.referencesE. EPRI, “Smart Grid Resource Center,” 2011.spa
dc.relation.referencesC. A. Díaz Andrade and J. C. Hernández, “Smart Grid: Las TICs y la modernización de las redes de energía eléctrica – Estado del Arte,” Revista S&T, vol. 9, no. 18, pp. 53–81, 2011.spa
dc.relation.referencesD. B. Unsal and T. Yalcinoz, “Applications of New Power Line Communication Model for Smart Grids,” International Journal of Computer and Electrical Engineering, vol. 7, no. 3, pp. 168–178, 2015.spa
dc.relation.referencesY. Yan, Y. Qian, H. Sharif, and D. Tipper, “A survey on smart grid communication infrastructures: Motivations, equirements and Challenges,” IEEE Communications Surveys and Tutorials, vol. 15, no. 1, pp. 5–20, 2013.spa
dc.relation.referencesF. Aalamifar, H. S. Hassanein, and G. Takahara, “Viability of Powerline Communication for the Smart Grid,” 2012 26th Biennial Symposium on Communications, QBSC 2012, pp. 19–23, 2012.spa
dc.relation.referencesV. Akarte, N. Punse, and A. Dhanorkar, “Power Line Communication Systems,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 2, no. 1, pp. 709–713, 2014.spa
dc.relation.referencesA. Horiguchi, A. Matsuzaki, S. Tanabe, Y. Mizugai, M. Oya, S. Ojima, Y. Hori, T. Akitomi, T. Kikuchi, T. Shimomura, H. Okamoto, Y. Murata, T. Kimura, and W. Matsumoto, “High Speed Power Line Communication Technology,” tech. rep., Mitsubishi electric Advance, 2005.spa
dc.relation.referencesH. Paz Penagos, “Ruido e interferencia en canales de comunicaciones por línea de distribución eléctrica,” Revista cientíica y tecnológica de la Universidad del Valle, vol. 17, no. 1, pp. 223–231, 2009.spa
dc.relation.referencesH. Paz Penagos, “Modulación por división de frecuencia ortogonal para minimizar el ruido en aplicaciones Smart Grid sobre comunicaciones por línea de potencia,” ITECKNE, vol. 10, no. 2, pp. 178–189, 2013.spa
dc.relation.referencesM. Cordero Limón, “Técnicas de estimación de canal en la capa física Wireless MAN -OFDM de la norma IEEE 802.16e,” 2009.spa
dc.relation.referencesR. Schmogrow, S. Wolf, B. Baeuerle, D. Hillerkuss, B. Nebendahl, C. Koos, W. Freude, and J. Leuthold, “Nyquist frequency division multiplexing for optical communications,” in 2012 Conference on Lasers and Electro-Optics (CLEO), pp. 1–2, 2012.spa
dc.relation.referencesJ. Lazaro, S. Knorr, B. Schrenk, I. Cano, V. Polo, J. Prat, A. Carena, and G. Bosco, “Digital nyquist wdm for access networks using limited bandwidth relective semiconductor optical amplifiers,” National Fiber Optic Engineers Conference (NFOEC), OSA Technical Digest, vol. paper JTh2A.57, 2012.spa
dc.relation.referencesZhongqi Pan, Junyi Wang, and Yi Weng, “Digital signal processing techniques in Nyquist-WDM transmission systems,” 2015 14th International Conference on Optical Communications and Networks (ICOCN), vol. 32, no. 2, pp. 1–3, 2015.spa
dc.relation.referencesA. Lowery, L. Zhuang, B. Corcoran, C. Zhu, and Y. Xie, “Photonic Circuit Topologies for Optical OFDM and Nyquist WDM,” Journal of Lightwave Technology, vol. 8724, no. c, pp. 1–1, 2016.spa
dc.relation.referencesL. Farias, M. Barreto, M. Leme, and S. Stevan, “Technical feasibility and performance analysis of g3-plc standard for monitoring in industrial environment,” IEEE LatinAmerica Transactions, vol. 14, no. 10, pp. 4241–4248, 2016.spa
dc.relation.referencesX. López Quiroz and C. Mora Martínez, “ANÁLISIS DE TÉCNICAS DE MODULACIÓN ADAPTIVA EN REDES INALÁMBRICAS DE BANDA ANCHA (IEEE 802.16, WIMAX),” 2006.spa
dc.relation.referencesI. H. H. HHI, “OFDM / Nyquist WDM.”spa
dc.relation.referencesUniversidad Nacional de Quilmes, “TEORÍA DE LAS TELECOMUNICACIONES,” 2008.spa
dc.relation.referencesM. T. Cerón López, “FUNDAMENTOS DE TELECOMUNICACIONES - MULTIPLEXACIÓN,” 2011.spa
dc.relation.referencesA. Artés Rodríguez, F. Pérez González, J. Cid Sueiro, R. López Valcarce, C. Mosquera Nartallo, and F. Pérez Cruz, Comunicaciones digitales, 2012.spa
dc.relation.referencesCiena Corporation, “Qué es wdm o dwdm?,” 2022.spa
dc.relation.referencesUniversidad del Azuay, “Multiplexación,” 2022.spa
dc.relation.referencesN. E. Hernández Romero, ANALISIS DE OPCIONES TECNOLOGICAS DE MODULACION Y MULTIPLEXACION EN LAS REDES OPTICAS DE 100 Gbps Y MAS. PhD thesis, UNIVERSIDAD AUSTRAL DE CHILE, 2015.spa
dc.relation.referencesS. Knorr, “Nyquist WDM for FTTH Applications using Relective Semiconductor Optical Amplifiers,” 2011.spa
dc.relation.referencesM. A. SHOAIE, “Optical Signal Processing and Pulse Shaping for Wavelength Multiplexed High Speed Communication Systems,” 2016.spa
dc.relation.referencesM. d. C. Clemente Medina, Modulación Adaptativa y Diversidad en canales de Comunicaciones Acústicas Subacuáticas . PhD thesis, Universidad de Málaga – Málaga, Spain, 2013.spa
dc.relation.referencesG. Llano Ramírez, Modelado en frecuencia del canal UWB y su aplicación en el análisis de técnicas de modulación adaptativa en sistemas MB-OFDM UWB para redes WPAN. PhD thesis, Universidad Politécnica de Valencia, 2010.spa
dc.relation.referencesNuWaves Engineering, “Application note an-005: Understanding constellation diagrams and how they are used,” tech. rep., NuWaves Engineering, 2019.spa
dc.relation.referencesF. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ru ni, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Communications Surveys Tutorials, vol. 21, no. 2, pp. 1383–1408, 2019.spa
dc.relation.referencesB. Mahesh, “Machine learning algorithms - a review,” International Journal of Science and Research, vol. 9, no. 1, pp. 381–386, 2019.spa
dc.relation.referencesY. R. Zheng, “Channel estimation and phase-correction for robust underwater acoustic communications,” in Proceedings - IEEE Military Communications Conference MIL-COM, 2007.spa
dc.relation.referencesS. Singh and N. Singh, “Nonlinear efects in optical ibers: Origin, management and applications,” Progress In Electromagnetics Research (PIER), vol. 73, pp. 249–275, 2007.spa
dc.relation.referencesW. Stark, Efects of Multipath Fading in Wireless Communication Systems. 06 2003.spa
dc.relation.referencesE. Lee and D. Messerschmitt, Digital Communication 2nd Ed. Springer, Boston, MA, 2011spa
dc.relation.referencesR. Zeng, T. Liu, X. Yu, and Z. Zhang, “Novel channel quality indicator prediction scheme for adaptive modulation and coding in high mobility environments,” IEEE Access, vol. 7, pp. 11543–11553, 2019.spa
dc.relation.referencesA. García Marqués, Cuantiicación del Estado del Canal para la Minimización de la Potencia en Sistemas con Transmisores Adaptativos. PhD thesis, Universidad Carlos III de Madrid – Escuela Politécnica Superior – Madrid, Spain, 2007.spa
dc.relation.referencesUmesha G.B. and M. Shanmukha Swamy, “Bit loading with ber-constraint for multicarrier systems,” International Journal of Engineering Research & Technology (IJERT), vol. 5, no. 1, pp. 15–19, 2017.spa
dc.relation.referencesC. Prieto del Amo, Estimación de Canal y Desplazamiento de Frecuencia en Sistemas MIMO-OFDM con Preijo Cíclico Insuiciente. PhD thesis, Universidad Carlos III de Madrid – Escuela Politécnica Superior – Madrid, Spain, 2015.spa
dc.relation.referencesD. Mavares T., Estimación de Canal y Selección Adaptativa de Código Espacio-Tiempo en Sistemas de Diversidad en Transmisión. PhD thesis, Universidad de Cantabria –Cantabria, Spain, 2006.spa
dc.relation.referencesX. Li, F. Dong, S. Zhang, and W. Guo, “A survey on deep learning techniques in wireless signal recognition,” Hindawi Wireless Communications and Mobile Computing, vol. Article ID 5629572, p. 12, 2019.spa
dc.relation.referencesG. Foschini, R. Gitlin, and S. Weinstein, “Optimization of two-dimensional signal constellations in the presence of gaussian noise,” IEEE Transactions on Communications, vol. 22, no. 1, pp. 28–38, 1974.spa
dc.relation.referencesM. Fuentes, Non-Uniform Constellations for Next-Generation Digital Terrestrial Broadcast Systems. PhD thesis, Universitat Politècnica de València – Valencia, Spain, 2017.spa
dc.relation.referencesM. Reza Khanzadi, Phase Noise in Communication Systems Modeling, Compensation, and Performance Analysis. PhD thesis, Chalmers University of Technology – Göteborg, Sweden, 2015.spa
dc.relation.referencesY. Gao, E. Ha, A. Lau, C. Lu, X. Xu, and L. Li, “Non-data-aided and universal cycle slip detection and correction for coherent communication systems,” Optics Express, vol. 22, no. 25, pp. 31167–31179, 2014.spa
dc.relation.referencesN. Guerrero Gonzalez, D. Zibar, A. Caballero, and I. Tafur Monroy, “Experimental 2.5-Gb/s QPSK WDM phase-modulated radio-over-iber link with digital demodulation by a K -means algorithm,” IEEE Photonics Technology Letters, vol. 22, no. 5, pp. 335–337, 2010.spa
dc.relation.referencesC. L. Cortés Cortés and N. Guerrero González, “Caracterización de la respuesta en fase y compensación de fase en sistemas multiportadora para canales con respuesta en frecuencia no uniforme,” Revista Ingenierías Universidad de Medellín, vol. 19, pp. 167 – 185, 06 2020.spa
dc.relation.referencesM. Zimmermann and K. Dostert, “A Multipath Model for the Powerline Channel,” IEEE Transactions on Communications, vol. 50, no. 4, pp. 553–559, 2002.spa
dc.relation.referencesK. Sharma and L. M. Saini, “Power-line communications for smart grid: Progress, challenges, opportunities and status,” Renewable and Sustainable Energy Reviews, vol. 67, pp. 704–751, 2017.spa
dc.relation.referencesC. Cano, A. Pittolo, D. Malone, L. Lampe, A. M. Tonello, and A. G. Dabak, “State of the art in power line communications: From the applications to the medium,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 7, pp. 1935–1952, 2016.spa
dc.relation.referencesP. Fränti and S. Sieranoja, “How much can k-means be improved by using better initialization and repeats?,” Pattern Recognition, vol. 93, pp. 95–112, 2019.spa
dc.relation.referencesO. M. Shekoni, A. N. Hasan, and T. Shongwe, “Applications of artiicial intelligence in powerline communications in terms of noise detection and reduction: a review,” Australian Journal of Electrical and Electronics Engineering, vol. 15, no. 1-2, pp. 29–37, 2018.spa
dc.relation.referencesA. M. Tonello, N. A. Letizia, D. Righini, and F. Marcuzzi, “Machine learning tips and tricks for power line communications,” IEEE Access, vol. 7, pp. 82434–82452, 2019.spa
dc.relation.referencesF. J. Cañete, K. Dostert, S. Galli, M. Katayama, L. Lampe, M. Lienard, S. Mashayekhi, D. G. Michelson, M. Nassar, R. Pighi, A. Pinomaa, M. Raugi, A. M. Tonello, M. Tucci, and F. Versolatto, Power Line Communications: Principles, Standards and Applications from Multimedia to Smart Grid, Second Edition. Hoboken, NJ, USA. Wiley, 2016.spa
dc.relation.referencesK. Dostert, M. Girotto, L. Lampe, R. Raheli, D. Rieken, T. G. Swart, A. M. Tonello, A. J. H. Vinck, and S. Weiss, Power Line Communications: Principles, Standards and Applications from Multimedia to Smart Grid, Second Edition. Hoboken, NJ, USA. Wiley, 2016.spa
dc.relation.referencesC. L. Cortés Cortés, S. X. Carvajal Quintero, and N. Guerrero González, “Demand side management system characterization for residential users in manizales city,” IEEE Latin America Transactions, vol. 19, p. 378–384, Jun. 2021.spa
dc.relation.referencesC. L. Cortés Cortés, M. A. Montaño Argote, A. M. Osorio, and N. Guerrero González, “Diseño de una red backhaul autogestionable para conectividad rural en sucre - Colombia,” Revista UIS Ingenierías, vol. 20, no. 1, pp. 67–78, 2021.spa
dc.relation.referencesUbiquiti Networks Inc, “Connecting everything everywhere,” 2021.spa
dc.relation.referencesC. A. Herter Jr., “The electromagnetic spectrum: A critical natural resource,” Natural resources journal, vol. 25, pp. 651–663, 1985.spa
dc.relation.referencesM. Zahid and Z. Meng, “Recent advances in neural network techniques for channel equalization: A comprehensive survey,” 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE), vol. Southend, United Kingdom, pp. 178–182, 2018.spa
dc.relation.referencesL. Wong, W. Headley, and A. Michaels, “Estimation of transmitter i/q imbalance using convolutional neural networks,” 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), vol. Las Vegas, NV, pp. 948–955, 2018.spa
dc.relation.referencesL. Zhang and L.-L. Yang, Machine Learning for Future Wireless Communications. Wiley-IEEE Press, Hoboken, NJ, 2020.spa
dc.relation.referencesJ. Jargon, X. Wu, H. Choi, Y. Chung, and A. Willner, “Optical performance monitoring of qpsk data channels by use of neural networks trained with parameters derived from asynchronous constellation diagrams,” Optics Express, vol. 18, no. 9, pp. 4931–4938, 2010.spa
dc.relation.referencesX. Liu, D. Yang, and A. Gamal, “Deep neural network architectures for modulation classification,” 2017 51st Asilomar Conference on Signals, Systems, and Computers, vol. Pacific Grove, CA, pp. 915–919, 2017.spa
dc.relation.referencesF. Khan, K. Zhong, X. Zhou, W. Al-Arashi, C. YU, C. LU, and A. Tao Lau, “Joint osnr monitoring and modulation format identiication in digital coherent receivers using deep neural networks,” Optics Express, vol. 25, no. 15, pp. 17767–17776, 2017.spa
dc.relation.referencesY. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 4074–4077, 2019.spa
dc.relation.referencesT. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Intelligent adaptive coherent optical receiver based on convolutional neural network and clustering algorithm,” Journal of Optical Communications and Networking, vol. 11, no. 1, pp. A52–A59, 2019.spa
dc.relation.referencesS. Zhang, F. Yaman, Nakamura, V. Kamalov, L. Jovanovski, V. Vusirikala, E. Mateo, Y. Inada, and T. Wang, “Field and lab experimental demonstration of nonlinear impairment compensation using neural networks,” Nature Communications, vol. 10, no. 3033, pp. 1–8, 2019.spa
dc.relation.referencesG. Song, M. Jang, and D. Yoon, “Cnn-based modulation classiication for ofdm signal,” in 2021 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1326–1328, 2021.spa
dc.relation.referencesY. Mao, M.-L. Zhu, T. Sun, Y.-Y. Dong, and C.-X. Dong, “Automatic modulation classiication based on snr estimation via two-stage convolutional neural networks,” in 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 294–298, 2021.spa
dc.relation.referencesZ. An, T. Zhang, M. Shen, E. De Carvalho, B. Ma, C. Yi, and T. Song, “Series-constellation feature based blind modulation recognition for beyond 5g mimo-ofdm systems with channel fading,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 793–811, 2022.spa
dc.relation.referencesJ. Zhang, W. Chen, M. Gao, Y. Ma, Y. Zhao, W. Chen, and G. Shen, “Intelligent adaptive coherent optical receiver based on convolutional neural network and clustering algorithm,” Optics Express, vol. 26, no. 14, pp. 18684–18698, 2018.spa
dc.relation.referencesS. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y.-D. Yao, “Modulation classiication based on signal constellation diagrams and deep learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 718–727, 2019.spa
dc.relation.referencesK. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Applied Sciences MDPI, vol. 10, no. 1166, pp. 1–14, 2020.spa
dc.relation.referencesD. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Optics Express, vol. 25, no. 15, pp. 32188–32198, 2017.spa
dc.relation.referencesC. L. Cortés and N. Guerrero González, “Constellation diagram processing with convolutional neural networks for channel phase response estimation,” Computer Communications, vol. 180, pp. 89–96, 2021.spa
dc.relation.referencesY. Xue, N. Ray, J. Hugh, and G. Bigras, “Cell counting by regression using convolutional neural network,” in Computer Vision – ECCV 2016 Workshops (G. Hua and H. Jégou, eds.), (Cham), pp. 274–290, Springer International Publishing, 2016.spa
dc.relation.referencesJ. Brownlee, “Deep learning with time series forecasting,” 2017.spa
dc.relation.referencesMathWorks ®, “Train convolutional neural network for regression,” 2017.spa
dc.relation.referencesH. Z. Ur Rehman and S. Lee, “Automatic image alignment using principal component analysis,” IEEE Access, vol. 6, pp. 72063–72072, 2018.spa
dc.relation.referencesL. Frenzel Jr, Handbook of Serial Communications Interfaces A Comprehensive Compendium of Serial Digital Input/output (I/o) Standards. Neunes, Ed. Elsevier, 2016.spa
dc.relation.referencesC. L. Cortés and N. Guerrero-González, “Fast deep learning based multicarrier phase response estimation in non-flat frequency response channels,” in OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF), p. SpTh2I.2, Optical Society of America, 2020.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc530 - Física::537 - Electricidad y electrónicaspa
dc.subject.proposalRespuesta de fasespa
dc.subject.proposalTécnicas de modulaciónspa
dc.subject.proposalVariaciones de fasespa
dc.subject.proposalProcesamiento de imágenesspa
dc.subject.proposalMultiplexación por división en frecuenciaspa
dc.subject.proposalForma de pulsospa
dc.subject.proposalDiagrama de constelaciónspa
dc.subject.proposalPhase responseeng
dc.subject.proposalModulation techniqueseng
dc.subject.proposalPhase offseteng
dc.subject.proposalImage processingeng
dc.subject.proposalFrequecy division multiplexingeng
dc.subject.proposalPulse shapeeng
dc.subject.proposalConstellation diagrameng
dc.subject.unescoInnovación científicaspa
dc.subject.unescoScientific innovationseng
dc.titleMetodología de estimación automática multiportadora de respuesta de canal de transmisión de datosspa
dc.title.translatedMulticarrier automatic estimation methodology of data transmission channel responsespa
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentImagespa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentBibliotecariosspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1053776114.2023.pdf
Tamaño:
30.23 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Doctorado en Ingeniería - Automática

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción: