Artículo
Unsupervised and Computationally Lightweight Spectrum Sensing in IoT Devices †
Autor/es | Martín Clemente, Rubén
Antonio Zarzoso, Vicente |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2022 |
Fecha de depósito | 2023-03-28 |
Publicado en |
|
Resumen | Principal component analysis (PCA) is a widespread technique in data analysis. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA due to its greater robustness to outliers. The ... Principal component analysis (PCA) is a widespread technique in data analysis. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA due to its greater robustness to outliers. The present work shows that, with a whitening step, L1-PCA can perform spectrum sensing and modulation recognition in IoT applications. Numerical experiments confirm this finding. |
Agencias financiadoras | Consejería de Transformación Económica, Industria, Conocimiento y Universidades. Junta de Andalucía. |
Identificador del proyecto | Proyecto ACACIA. US-1264994. |
Cita | Martín Clemente, R. y Antonio Zarzoso, V. (2022). Unsupervised and Computationally Lightweight Spectrum Sensing in IoT Devices †. Engineering Proceedings, 27 (1), 76. https://doi.org/10.3390/ecsa-9-13159. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
EP_2022_Martin_Unsupervised_OA.pdf | 427.3Kb | [PDF] | Ver/ | |