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ML aided context feature extraction for cognitive radio

Author
Bolea, L.; Perez-Romero, J.; Agusti, R.
Type of activity
Journal article
Journal
Computer networks
Date of publication
2013-10
Volume
57
Number
17
First page
3713
Last page
3727
DOI
https://doi.org/10.1016/j.comnet.2013.08.016 Open in new window
Repository
http://hdl.handle.net/2117/21118 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S138912861300282X Open in new window
Abstract
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 ...
Citation
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.
Keywords
Feature extraction, Image processing, Maximum likelihood estimation, Radio transmission, Sensor networks, Transmitters
Group of research
CCABA - Advanced Broadband Communications Center
GRCM - Mobile Communication Reserach Group

Participants