2026-06-03T11:42:58Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/275612026-02-03T12:27:38Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Luo Chen, Hao Qiang
author
Khatib, Emil Jatib
author
Sethi, Deepak
author
Cruz, Eduardo
author
Arostegui, Asier
author
Martín, Raúl
author
Barco-Moreno, Raquel
2023-09-18T12:38:09Z
2023-09-18T12:38:09Z
2023
https://hdl.handle.net/10630/27561
Currently, the increase of location aware services and network management has driven the demand for user location estimation schemes, although it is not usually available to operators. Moreover, commercial networks have limited access to specific user related metrics. In general, solutions with Machine Learning (ML) have reached high precisions, but only in a trained scenario, and with difficulties in predicting unseen areas. The approach proposed here solves the above limitation by a reference coordinate conversion, to obtain relative polar positions which create scenario agnostic models, and whose performance is demonstrated using a dataset recollected from a commercial mobile network.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Telecomunicaciones
Localización de usuarios con coordenadas polares.
conference output
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URL
https://riuma.uma.es/bitstreams/b36d1b66-3b5f-4767-a13b-f3e70e4718fd/download
File
MD5
5492ade620131413fc014525963bc314
107890
application/pdf
Resumen_URSI_2023_HQLC.pdf