ML aided context feature extraction for cognitive radio

Authors:
Liliana Bolea;Jordi Pérez-Romero;Ramón Agustí
Affiliations:
-;-;-
Venue:
Computer Networks: The International Journal of Computer and Telecommunications Networking
Year:
2013

Citing 6
Cited 0

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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 applying image processing and a Maximum Likelihood estimation algorithm over the set of measurements to identify the existing transmitters in the scenario and their parameters. The proposed method can provide a quite similar performance than a classical ML method, in terms of average estimation errors while at the same time reducing the computation time in about three orders of magnitude, for the considered case study.