Prado-Alvarez, D.; Inca-Sanchez, SA.; Martín-Sacristán, D.; Monserrat Del Río, JF. (2019). Comparison of Optimization Methods for Aerial Base Station Placement with Users Mobility. IEEE. 485-489. https://doi.org/10.1109/EuCNC.2019.8802053
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/131278
Title:
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Comparison of Optimization Methods for Aerial Base Station Placement with Users Mobility
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Author:
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Prado-Alvarez, Danaisy
Inca-Sanchez, Saul Adrian
Martín-Sacristán, David
Monserrat del Río, Jose Francisco
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UPV Unit:
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Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia
Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
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Issued date:
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Abstract:
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Aerial base stations have been recently considered in the deployment of wireless networks. Finding the optimal position for one or multiple aerial base stations is a complex problem tackled by several works. However, just ...[+]
Aerial base stations have been recently considered in the deployment of wireless networks. Finding the optimal position for one or multiple aerial base stations is a complex problem tackled by several works. However, just a few works consider the mobility of the users which makes necessary an online optimization to follow the changes in the scenario where the optimization is performed. This paper deals with the online optimization of an aerial base station placement considering different types of users mobility and three algorithms: a Q-learning technique, a Gradient-based solution and a Greedy-search solution. Our objective is to minimize in an urban environment the path loss of the user at street level with the highest path loss. Simulation results show that the performance of the three methods is similar when a high number of users move randomly and uniformly around the scenario under test. Nevertheless, in some situations when the number of users is reduced or when the users move together in a similar direction, both Gradient and Greedy algorithms present a significantly better performance than the Q-learning method.
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Copyrigths:
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Reserva de todos los derechos
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ISBN:
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978-1-7281-0546-8
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DOI:
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10.1109/EuCNC.2019.8802053
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Publisher:
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IEEE
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Publisher version:
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https://doi.org/10.1109/EuCNC.2019.8802053
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Conference name:
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2019 European Conference on Networks and Communications (EuCNC)
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Conference place:
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Valencia, Spain
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Conference date:
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Junio 18-21,2019
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Project ID:
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info:eu-repo/grantAgreement/EC/H2020/766231/EU/mmWave Communications in the Built Environments/
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Thanks:
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The work of Danaisy Prado was supported by the H2020 Marie Curie Program, with Project Grant No. 766231 WAVECOMBE - ITN - 2017
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Type:
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Comunicación en congreso
Capítulo de libro
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