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ML aided context feature extraction for cognitive radio

Autor
Bolea, L.; Perez-Romero, J.; Agusti, R.
Tipus d'activitat
Article en revista
Revista
Computer networks
Data de publicació
2013-10
Volum
57
Número
17
Pàgina inicial
3713
Pàgina final
3727
DOI
https://doi.org/10.1016/j.comnet.2013.08.016 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/21118 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S138912861300282X Obrir en finestra nova
Resum
This paper addresses the estimation of different context features of a primary user network, such as transmitters’ positions, antenna patterns and directions, and propagation model characteristics. It is based on radio signal strength measurements obtained by a sensor network without any prior knowledge about the configuration of the primary transmitters in terms of antenna types or propagation model. A Maximum Likelihood Aided Context Feature Extraction (MLACFE) method is introduced based on ...
Citació
Bolea, L.; Perez, J.; Agusti, R. ML aided context feature extraction for cognitive radio. "Computer networks", Octubre 2013, vol. 57, núm. 17, p. 3713-3727.
Paraules clau
Radio Transmission, Feature Extraction, Image Processing, Maximum Likelihood Estimation, Sensor Networks, Transmitters
Grup de recerca
CCABA - Centre de Comunicacions Avançades de Banda Ampla
GRCM - Grup de Recerca en Comunicacions Mòbils

Participants