Outlier Detection and Spectrum Feature Extraction Based on
Nearest-Neighbors Correlation and Random Forest Algorithm
Abstract
Most spectrum surveys conducted worldwide demonstrate that the
radio-electric spectrum in use at any given location and instant of time
is below 25%. Current spectrum management policies and spectrum
utilization inefficiency are becoming unsustainable for the future
development of radio technologies and services. In this context, dynamic
spectrum access is a promising technique for improving spectrum
utilization efficiency. A key scientific gap is identifying inaccurate
spectrum data from hidden nodes that are not homogeneously distributed
in the spatial domain and dynamically vary in time and frequency. For
bridging this gap, our paper presents the research results of a spectrum
feature extraction algorithm based on multi-correlation and Random
Forest. Our algorithm is capable of estimating the spectrum utilization
pattern in the spatial and frequency domain with a reliability up to
92% for a real heterogeneous networking scenario.