ML aided context feature extraction for cognitive radio
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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.
CitationBolea, L.; Perez, J.; Agusti, R. ML aided context feature extraction for cognitive radio. "Computer networks", Octubre 2013, vol. 57, núm. 17, p. 3713-3727.
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