TY  - INPR
U1  - Preprint
A1  - Weber, Christian
A1  - Minin, Peter
A1  - Felhauer, Tobias
A1  - Christ, Andreas
A1  - Schüssele, Lothar
T1  - Data clustering algorithm for channel segmentation in a radio monitoring system
T2  - IET Communications
N2  - The detection of signals and the estimation of signal bandwidth is a perpetual topic in radio communication systems. Both issues are extremely challenging, since the wireless channel is unreliable in nature. A radio monitoring system faces the most difficult conditions in this task; it normally scans a wide frequency range of several hundred MHz and has to detect a multitude of different signals. Owing to the computational costs, the radio monitoring systems use nowadays mainly energy detectors based on fast Fourier transform spectrum analysers and a static threshold, defined by a previous noise estimation. A refined algorithm based on the self-splitting competitive learning (SSCL) clustering is presented that quantises the power spectral density (PSD) according to the present signal power levels. The quantisation of the PSD results in a promising channel segmentation. In contrast to the traditional threshold evaluation, this approach is independent of a previously assumed noise estimation and therefore more robust against noise level and noise distribution changes. The presented definition of the essential cluster validity criterion is key for a successful channel segmentation. Furthermore, the novel postprocessing of the clustering result introduced in this study evaluates the progression of the PSD data and significantly improves the channel segmentation.
KW  - Funktechnik
KW  - Überwachung
KW  - Algorithmus
Y1  - 2014
SN  - 1751-8628
SS  - 1751-8628
U6  - https://doi.org/10.1049/iet-com.2013.1104
DO  - https://doi.org/10.1049/iet-com.2013.1104
VL  - 8
IS  - 18
SP  - 3308
EP  - 3317
ER  -