Data clustering algorithm for channel segmentation in a radio monitoring system. Issue 18 (1st December 2014)

• Record Type: Journal Article
• Authors: Weber, Christian; Peter, Martin; Felhauer, Tobias; Christ, Andreas; Schuessele, Lothar
• Is Part Of: IET communications. Volume 8:Issue 18(2014)
• Page Start: 3308
• Page End: 3317
• Publication Date: 2014-12-01
• Subjects: pattern clustering -- wireless channels -- signal detection -- monitoring -- cognitive radio -- fast Fourier transforms -- spectral analysis -- learning (artificial intelligence) -- quantisation (signal); data clustering algorithm -- channel segmentation -- radio monitoring system -- signal detection -- signal bandwidth estimation -- wireless channel -- wireless communication systems -- cognitive radio device -- fast Fourier transform spectrum analysers -- energy detector -- self‐splitting competitive learning clustering -- power spectral density -- PSD quantisation -- threshold evaluation -- noise estimation -- cluster validity criterion; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2013.1104
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : 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.