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Modelos de pérdidas de propagación en redes de internet de las cosas: una mirada desde el aprendizaje de máquina

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Date
2020-09-24
Author
González-Palacio, Mauricio
TY - GEN T1 - Modelos de pérdidas de propagación en redes de internet de las cosas: una mirada desde el aprendizaje de máquina AU - González-Palacio, Mauricio Y1 - 2020-09-24 UR - http://hdl.handle.net/11407/5850 PB - Vicerrectoría de Investigaciones AB - A common task when planning a wireless network is the analysis of propagation losses between two transceivers. Different models, from theoretical and empirical natures are proposed in the literature; however, some of them are difficult to be parametrized, and others are thought for very specific scenarios. In this work, we perform a comparison between the simplified path loss lognormal shadow fading model versus a Support Vector Machine (SVM) regressor in a WLAN network. Results show that SVMs are more accurate predicting shadow fading effects ER - @misc{11407_5850, author = {González-Palacio Mauricio}, title = {Modelos de pérdidas de propagación en redes de internet de las cosas: una mirada desde el aprendizaje de máquina}, year = {2020-09-24}, abstract = {A common task when planning a wireless network is the analysis of propagation losses between two transceivers. Different models, from theoretical and empirical natures are proposed in the literature; however, some of them are difficult to be parametrized, and others are thought for very specific scenarios. In this work, we perform a comparison between the simplified path loss lognormal shadow fading model versus a Support Vector Machine (SVM) regressor in a WLAN network. Results show that SVMs are more accurate predicting shadow fading effects}, url = {http://hdl.handle.net/11407/5850} }RT Generic T1 Modelos de pérdidas de propagación en redes de internet de las cosas: una mirada desde el aprendizaje de máquina A1 González-Palacio, Mauricio YR 2020-09-24 LK http://hdl.handle.net/11407/5850 PB Vicerrectoría de Investigaciones AB A common task when planning a wireless network is the analysis of propagation losses between two transceivers. Different models, from theoretical and empirical natures are proposed in the literature; however, some of them are difficult to be parametrized, and others are thought for very specific scenarios. In this work, we perform a comparison between the simplified path loss lognormal shadow fading model versus a Support Vector Machine (SVM) regressor in a WLAN network. Results show that SVMs are more accurate predicting shadow fading effects OL Spanish (121)
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Abstract
A common task when planning a wireless network is the analysis of propagation losses between two transceivers. Different models, from theoretical and empirical natures are proposed in the literature; however, some of them are difficult to be parametrized, and others are thought for very specific scenarios. In this work, we perform a comparison between the simplified path loss lognormal shadow fading model versus a Support Vector Machine (SVM) regressor in a WLAN network. Results show that SVMs are more accurate predicting shadow fading effects
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http://hdl.handle.net/11407/5850
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