Mitigation of distortions in radio-over-fiber systems using machine learning

Introduction— The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireles...

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Autores:: Torres Vahos, Diego Fernando
Escobar, Alejandro
Díaz Rodriguez, Cristian Alexis
Granada Torres, Jhon James

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.eng.fl_str_mv	Mitigation of distortions in radio-over-fiber systems using machine learning
dc.title.translated.none.fl_str_mv	Mitigación de distorsiones en sistemas de radio-sobre-fibra usando aprendizaje automático
title	Mitigation of distortions in radio-over-fiber systems using machine learning
spellingShingle	Mitigation of distortions in radio-over-fiber systems using machine learning Asymmetrical demodulation Machine learning Millimeter wave band Radio-over-fiber Support vector machine Aprendizaje automático Banda de ondas milimétricas Demodulación asimétrica Máquina de soporte vectorial Radio-sobre-fibra
title_short	Mitigation of distortions in radio-over-fiber systems using machine learning
title_full	Mitigation of distortions in radio-over-fiber systems using machine learning
title_fullStr	Mitigation of distortions in radio-over-fiber systems using machine learning
title_full_unstemmed	Mitigation of distortions in radio-over-fiber systems using machine learning
title_sort	Mitigation of distortions in radio-over-fiber systems using machine learning
dc.creator.fl_str_mv	Torres Vahos, Diego Fernando Escobar, Alejandro Díaz Rodriguez, Cristian Alexis Granada Torres, Jhon James
dc.contributor.author.none.fl_str_mv	Torres Vahos, Diego Fernando Escobar, Alejandro Díaz Rodriguez, Cristian Alexis Granada Torres, Jhon James
dc.subject.proposal.eng.fl_str_mv	Asymmetrical demodulation Machine learning Millimeter wave band Radio-over-fiber Support vector machine
topic	Asymmetrical demodulation Machine learning Millimeter wave band Radio-over-fiber Support vector machine Aprendizaje automático Banda de ondas milimétricas Demodulación asimétrica Máquina de soporte vectorial Radio-sobre-fibra
dc.subject.proposal.spa.fl_str_mv	Aprendizaje automático Banda de ondas milimétricas Demodulación asimétrica Máquina de soporte vectorial Radio-sobre-fibra
description	Introduction— The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission. Objective— To improve the performance of a Radioover-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine. Methodology— A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation. Results— The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two orders of magnitude in comparison with conventional demodulation. Conclusions— Mitigation of distortions in terms of bit-error-rate is demonstrated in a Radio-over-Fiber system using nonsymmetrical demodulation by using the Support Vector Machine algorithm. Thus, the proposed technique can be suitable for future high-capacity access networks.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2023-06-23T22:20:37Z
dc.date.available.none.fl_str_mv	2023-06-23T22:20:37Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.citation.spa.fl_str_mv	D. Torres Vahos, A. Escobar Pérez, C. Diaz Rodríguez & J. Granada Torres, “Mitigation of Distortions in Radio-OverFiber Systems Using Machine Learning”, INGE CUC, vol. 17, no. 2, pp. 234–244, 2021. DOI: http://doi.org/10.17981/ ingecuc.17.2.2021.21
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dc.identifier.doi.none.fl_str_mv	10.17981/ ingecuc.17.2.2021.21
dc.identifier.eissn.spa.fl_str_mv	2382-4700
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
identifier_str_mv	D. Torres Vahos, A. Escobar Pérez, C. Diaz Rodríguez & J. Granada Torres, “Mitigation of Distortions in Radio-OverFiber Systems Using Machine Learning”, INGE CUC, vol. 17, no. 2, pp. 234–244, 2021. DOI: http://doi.org/10.17981/ ingecuc.17.2.2021.21 0122-6517 10.17981/ ingecuc.17.2.2021.21 2382-4700 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/10262 https://repositorio.cuc.edu.co/
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dc.relation.references.spa.fl_str_mv	[1] I. Guvenc, S. Gezici, Z. Sahinoglu & U. Kozat (Eds.), Reliable communications for short-range wireless systems.NY, USA, CUP, pp. 1–3, 2011. vv10.1017/CBO9780511974366 [2] R. A. González, “Diseño y simulación de arreglos de antena en la frecuencia de 72 ghz (banda–e) para empleo en redes móviles 5g,” in Desarrollo e innovacion en ingenieria, E. Serna, Ed., MED, CO, Fundacioniai, pp. 255–263, 2018. [3] J. Leyva y D. Beltrán, “La comunicación inalámbrica a través de la banda de los 60 ghz,,” RUS, vol. 8, no. 2, pp. 89–96, 2016. Disponible en https://rus.ucf.edu.cu/index.php/rus/article/view/373 [4] Nan Guo, R. C. Qiu, Shaomin S. Mo & Kazuaki Takahashi, “60- GHzMillimeter-Wave Radio: Principle, Technology, and New Results,” EURASIP J Wirel Commun Netw, pp. 1–8, 2007. https://doi. org/10.1155/2007/68253 [5] M. A. Ilgaz & B. Batagelj, “Proposal for distribution of a low-phase-noise oscillator signal in forthcoming fifth-generation mobile network by radio-over-fibre technology,” presented at 58th International Symposium, ELMAR, ZAD, HR, pp. 13–16, 2016. https://doi.org/10.1109/ELMAR.2016.7731744 [6] N. Kathpal & A. K. Garg, “Analysis of radio over fiber system for mitigating four-wave mixing effect,” Digit Commun Netw, vol. 6, no. 1, pp. 115–122, 2020. https://doi.org/10.1016/j.dcan.2019.01.003 [7] S. Chaudhary, D. Thakur & A. Sharma, “10 gbps-60 ghz rof transmission system for 5g applications,” J Opt Commun Netw, vol. 40, no. 3, pp. 281–284, 2019. https://doi.org/10.1515/joc-2017-0079 [8] J. J. Granada, C. M. Serpa, G. M. Varón, y N. Guerrero, “Hacia la próxima generación de sistemas de Radio sobre Fibra de banda ancha: retos tecnológicos en la banda de las ondas milimétricas,” Ing Des, vol. 29, no. 2, pp. 242–265, 2011. Disponible en https://rcientificas.uninorte.edu.co/index.php/ingenieria/ article/view/3627 [9] H. B. Kim & A. Wolisz, “A radio over fiber based wireless access network architecture for rural areas,” presented at WCNM, CiteSeer, DRS, GE, 2005. Disponible en http://www.eecs.tu-berlin.de/fileadmin/ fg112/Papers/RoFAccessISTFinal.pdf [10] S. Constaín, I. Mantilla, G. C. Rueda, L. F. Trujillo y J. Barrera, Plan 5g Colombia el futuro digital es de todos. BO, CO: Mintic, 2019. Recuperado de https://mintic.gov.co/micrositios/plan_5g//764/articles-162230_recurso_1.pdf [11] J. J. G. Torres, G. M. V. Durán & N. G. González, “Chromatic dispersion effects in a radio over fiber system with psk modulation and coherent detection,” presented at COLCOM, ICESI, CA, CO, pp. 1–6, 2012. https://doi.org/10.1109/ColComCon.2012.6233655 [12] N. Guerrero, D. Zibar, X. Yu & I. T. Monroy, “Optical phase-modulated radio-over-fiber links with kmeans algorithm for digital demodulation of 8psk subcarrier multiplexed signals,” presented at OFC, OSA/OPC, SD, CA, USA, 2010. https://doi.org/10.1364/OFC.2010.OML3 [13] M. Amini & E. Balarastaghi, “Universal neural network demodulator for software defined radio,” Int J Mach Learn Comput, vol. 1, no. 3, pp. 305–310, 2011. Available from http://www.ijmlc.org/papers/45- L335.pdf [14] Y. Huang, Y. Chen & J. Yu, “Nonlinearity mitigation of rof signal using machine learning based classifier,” presented at ACPC, OSA/OPC, SUI, GD, CHN, pp. 1–3, 2017. https://doi.org/10.1364/ACPC.2017. Su2A.28 [15] E. A. Fernandez, J. J. G. Torres, A. M. C. Soto & N. G. González, “Demodulation of m-ary non-symmetrical constellations using clustering techniques in optical communication systems,” presented at LACCI, IEEE, CTG, CO, pp. 1–6, 2016. https://doi.org/10.1109/LA-CCI.2016.7885720 [16] Y. Cui, M. Zhang, D. Wang, S. Liu, Z. Li & G.-K. Chang, “Bit-based support vector machine nonlinear detector for millimeter-wave radio-over-fiber mobile fronthaul systems,” Opt Express, vol. 25, no. 21, pp. 26186–26197, 2017. https://doi.org/10.1364/OE.25.026186 [17] J. Mitchell, “Radio over fiber networks: Advances and challenges,” presented at ECOC '09, IEEE, WIE, AUT, pp. 1–4, 2009. Available: https://conference.vde.com/ecoc-2009/about/Pages/About%20ECOC%20 2009.aspx [18] S. P. Singh & N. Singh, “Nonlinear Effects in Optical Fibers: Origin, Management and Applications,” PIER, vol. 73, pp. 249–275, 2007. https://doi.org/10.2528/PIER07040201 [19] G. D. V. Revelo, J. R. O. Arroyave, D. P. T. Vallejo & N. G. Gómez, “Descripción y análisis de desempeño de sistemas de radio sobre fibra con enlaces de 900mhz con modulaciones en fase y en cuadratura,” Rev Politec, vol. 9, no. 16, pp. 51–62, 2013. Recuperado de https://revistas.elpoli.edu.co/index.php/pol/article/ view/330 [20] J. Granada, A. Cárdenas & N. Guerrero, “A novel dispersion monitoring technique in w-band radio-overfiber signals using clustering on asynchronous histograms,” Ing Invest, vol. 34, no. 3, pp. 76–80, 2014. https://doi.org/10.15446/ing.investig.v34n3.42902 [21] J. Zander, “On the cost structure of future wideband wireless access,” presented at 47th Vehicular Technology, VTS, PHO, AZ, USA, pp. 1773–1776, 1997. https://doi.org/10.1109/VETEC.1997.605863 [22] D. Sinha, A. K. Verma & S. Kumar, “Software defined radio: Operation, challenges and possible solutions,” presented at 10th ISCO, ISCO, COVAI, IN, pp. 1–5, 2016. https://doi.org/10.1109/ISCO.2016.7727079 [23] M. Rico-Martinez, C. C. C. Vasquez, S. I. Rodriguez, G. M. V. Duran & I. T. Monroy, “Comparison of performance between OFDM and GFDM in a 3.5GHz band 5G hybrid Fiber-Wireless link using SDR,” presented at MWP, IEE, TLSE, FR, pp. 1–4, 2018. https://doi.org/10.1109/MWP.2018.8552915 [24] S. Agrawal & K. Sharma.5g millimeter wave (mmwave) communication system with software defined radio (sdr),” Data Brief ,vol. 23, pp. 8–15, 2016. https://doi.org/10.1016/j.dib.2018.12.003 [25] R. K. Miranda, J. P. C. da Costa, F. Roemer, F. Raschke, T. Eishima, Y. Nakamura & G. Del Galdo, “Implementation of improved software defined radio modulation scheme and command and telemetry software interface for small satellites in 5g systems,” presented at ICOF, OFDM, Essen, GE, pp. 1–7, 2016.Available from https://lasp.unb.br/wp-content/uploads/papers/ICOF16_Miranda.pdf [26] X. Xiong, W. Xiang, K. Zheng, H. Shen & X. Wei, “An open source SDR-based NOMA system for 5G networks,” IEEE Wirel Commun, vol. 22, no. 6, pp. 24–32, Dec. 2015. https://doi.org/10.1109/ MWC.2015.7368821 [27] H. M. Hizan, T. Kanesan, S. Kh. Sadon, A. Ibrahim, M. A. Ismail, S. M. Hassan, R. Sanusi, A. Yusof, F. Maskuriy & S. M. Mitani, “Multiservice wireless network testbed design using SDR and RoF platforms,” presented at APACE, AP/MTT/EMC, LGK, ML, pp. 369–372, 2016. https://doi.org/10.1109/ APACE.2016.7916462 [28] M. Rico-Martínez, A. Morales, V. Mehmeri, R. Puerta, M. Varón & I. T. Monroy, “Procedure to measure real time latency using software defined radio in a w-band fiber-wireless link,” Microw Opt Technol Lett, vol. 59, no. 12, pp. 3147–3151, 2017. https://doi.org/10.1002/mop.30904 [29] D. Konstantinou, A. Morales, I. Aghmari, S. Rommel, T. R. Raddo, U. Johannsen & I. T. Monroy, “Highspeed wireless access in forested rural areas using analog radio-over-fiber technology,” presented at LAOP, OSA, LI, PE, pp. 1–2, 2018. https://doi.org/10.1364/LAOP.2018.Tu2B.4 [30] J. David Cepeda, S. I. Rodríguez, M. Rico-Martínez, C. Daniel Muñoz, M. Varón & I. T. Monroy, “Performance evaluation of a real time OFDM radio over fiber system at 2.5 GHz using software defined radio SDR,” presented at IMOC, SBMO/IEEE MTT-S, Aguas de Lindoia, BR, pp. 1–5, 2017. https://doi. org/10.1109/IMOC.2017.8121094 [31] J. Sánchez, J. Nieto, M. Vázquez & V. Velázquez, “Umbral adaptivo para sistemas de comunicaciones ópticas inalámbricas por medio de algoritmos de agrupamiento,” RCS, vol. 108, pp. 127–134, 2015. https:// doi.org/10.13053/rcs-108-1-14 [32] V. Jakkula, Tutorial on support vector machine (svm).WSU. WA, USA, School of EECS, , 2006.Available from https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf [33] J. A. Resendiz, “Las Máquinas de Soporte Vectorial para identificación en Línea,” Tesis Maestria, IPN, CDMX, MX, 2002.Recuperado de https://www.ctrl.cinvestav.mx/~yuw/pdf/MaTesJAR.pdf
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Derechos de autor 2021 INGE CUChttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Torres Vahos, Diego FernandoEscobar, AlejandroDíaz Rodriguez, Cristian AlexisGranada Torres, Jhon James2023-06-23T22:20:37Z2023-06-23T22:20:37Z2021D. Torres Vahos, A. Escobar Pérez, C. Diaz Rodríguez & J. Granada Torres, “Mitigation of Distortions in Radio-OverFiber Systems Using Machine Learning”, INGE CUC, vol. 17, no. 2, pp. 234–244, 2021. DOI: http://doi.org/10.17981/ ingecuc.17.2.2021.210122-6517https://hdl.handle.net/11323/1026210.17981/ ingecuc.17.2.2021.212382-4700Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Introduction— The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission. Objective— To improve the performance of a Radioover-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine. Methodology— A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation. Results— The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two orders of magnitude in comparison with conventional demodulation. Conclusions— Mitigation of distortions in terms of bit-error-rate is demonstrated in a Radio-over-Fiber system using nonsymmetrical demodulation by using the Support Vector Machine algorithm. Thus, the proposed technique can be suitable for future high-capacity access networks.Introducción— El constante crecimiento de usuarios conectados a internet por medio de dispositivos móviles ha conllevado a incrementar la investigación en el paradigma de las redes híbridas conocido como Radio-sobreFibra. Estas redes aprovechan las ventajas del ancho de banda de la fibra óptica y la movilidad de las transmisiones inalámbricas, evitando el cuello de botella que se da por la conversión óptico a eléctrico. No obstante, la dispersión cromática propia de la fibra óptica genera distorsiones en la señal de radiofrecuencia modulada ópticamente, lo cual limita su alcance. Objetivo— Mejorar el desempeño de un sistema de radio sobre fibra en términos de la tasa de error de bit, usando demodulación no simétrica por medio del algoritmo de aprendizaje automático Máquina de Soporte Vectorial. Metodología— Se simula un sistema de Radio-sobreFibra en el software especializado VPIDesignSuite. Se transmiten señales de radiofrecuencia moduladas en formatos 16 y 64-QAM con diferentes anchos de línea de láser sobre fibra óptica. Se aplica el algoritmo Máquina de Soporte Vectorial para la demodulación de la señal. Resultados— La implementación del algoritmo de aprendizaje automático para la demodulación de la señal mejora significativamente el desempeño de la red permitiendo alcanzar los 30 km de transmisión por fibra óptica. Esto implica una reducción de la tasa de error de bit hasta en dos órdenes de magnitud en comparación con la demodulación tradicional. Conclusiones— Se demuestra que con el uso de umbrales asimétricos usando algoritmo de Máquina de Soporte Vectorial se logran mitigar distorsiones en términos de la tasa de error de bit. Así, esta técnica se hace atractiva para futuras redes de acceso de alta capacidad.11 páginasapplication/pdfengCorporación Universidad de la CostaColombiahttps://revistascientificas.cuc.edu.co/ingecuc/article/view/3467Mitigation of distortions in radio-over-fiber systems using machine learningMitigación de distorsiones en sistemas de radio-sobre-fibra usando aprendizaje automáticoArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85INGE CUC[1] I. Guvenc, S. Gezici, Z. Sahinoglu & U. Kozat (Eds.), Reliable communications for short-range wireless systems.NY, USA, CUP, pp. 1–3, 2011. vv10.1017/CBO9780511974366[2] R. A. González, “Diseño y simulación de arreglos de antena en la frecuencia de 72 ghz (banda–e) para empleo en redes móviles 5g,” in Desarrollo e innovacion en ingenieria, E. Serna, Ed., MED, CO, Fundacioniai, pp. 255–263, 2018.[3] J. Leyva y D. Beltrán, “La comunicación inalámbrica a través de la banda de los 60 ghz,,” RUS, vol. 8, no. 2, pp. 89–96, 2016. Disponible en https://rus.ucf.edu.cu/index.php/rus/article/view/373[4] Nan Guo, R. C. Qiu, Shaomin S. Mo & Kazuaki Takahashi, “60- GHzMillimeter-Wave Radio: Principle, Technology, and New Results,” EURASIP J Wirel Commun Netw, pp. 1–8, 2007. https://doi. org/10.1155/2007/68253[5] M. A. Ilgaz & B. Batagelj, “Proposal for distribution of a low-phase-noise oscillator signal in forthcoming fifth-generation mobile network by radio-over-fibre technology,” presented at 58th International Symposium, ELMAR, ZAD, HR, pp. 13–16, 2016. https://doi.org/10.1109/ELMAR.2016.7731744[6] N. Kathpal & A. K. Garg, “Analysis of radio over fiber system for mitigating four-wave mixing effect,” Digit Commun Netw, vol. 6, no. 1, pp. 115–122, 2020. https://doi.org/10.1016/j.dcan.2019.01.003[7] S. Chaudhary, D. Thakur & A. Sharma, “10 gbps-60 ghz rof transmission system for 5g applications,” J Opt Commun Netw, vol. 40, no. 3, pp. 281–284, 2019. https://doi.org/10.1515/joc-2017-0079[8] J. J. Granada, C. M. Serpa, G. M. Varón, y N. Guerrero, “Hacia la próxima generación de sistemas de Radio sobre Fibra de banda ancha: retos tecnológicos en la banda de las ondas milimétricas,” Ing Des, vol. 29, no. 2, pp. 242–265, 2011. 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Resendiz, “Las Máquinas de Soporte Vectorial para identificación en Línea,” Tesis Maestria, IPN, CDMX, MX, 2002.Recuperado de https://www.ctrl.cinvestav.mx/~yuw/pdf/MaTesJAR.pdf244234217Asymmetrical demodulationMachine learningMillimeter wave bandRadio-over-fiberSupport vector machineAprendizaje automáticoBanda de ondas milimétricasDemodulación asimétricaMáquina de soporte vectorialRadio-sobre-fibraPublicationORIGINALMitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning.pdfMitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning.pdfArtículoapplication/pdf2328720https://repositorio.cuc.edu.co/bitstreams/a47dc272-6744-4952-9d17-5dd9d0f65446/downloadee6b09a56c4b8dd7c7304d7c5b706398MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/833f2ef5-7fad-44f7-ae9f-aba15d712228/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTMitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning.pdf.txtMitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning.pdf.txtExtracted texttext/plain35369https://repositorio.cuc.edu.co/bitstreams/b185b66f-73fe-4c97-9a9f-2cd57e7458c4/download11d5007b27991a3f53e7c1b4a37e1627MD53THUMBNAILMitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning.pdf.jpgMitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning.pdf.jpgGenerated Thumbnailimage/jpeg13164https://repositorio.cuc.edu.co/bitstreams/e4d500bf-2dfa-4026-b7b0-d83ab05d1799/downloadc8f3b176f7a20e81a0ff33c1425d6d98MD5411323/10262oai:repositorio.cuc.edu.co:11323/102622024-09-17 10:48:55.984https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos de autor 2021 INGE CUCopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


Mitigation of distortions in radio-over-fiber systems using machine learning

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