Random Spectral Sampling for Compliance Enforcement in Dynamic Spectrum Access Networks

Abstract

A similarity-based spectral stratification technique is proposed for automatic, real-time characterization of spectrum occupancy in wideband sensing, using random spectral sampling. Sub-bands with similar properties, as defined by proximity metrics, are grouped into strata during any statistically stationary interval. A subset of each stratum is then sampled for estimating properties of interest. This approach can reduce requirements for sampling and processing of the entire wideband. The technique is investigated in the context of spectrum monitoring for the compliance enforcement function, which is an essential component in spectrum management. The technique is explored, to estimate bandwidth occupancy using software defined radio hardware, in the ISM band as well as for 24 h of measurements spanning 800 MHz–1 GHz. Results suggest that the proposed technique improves on previous non-adaptive stratification techniques for random spectral sampling, and was comparable in clustering performance to k-means clustering, while executing in a deterministic time without the need for iterations or setting the initial number of segments to cluster, as required for k-means. The proposed stratification technique therefore offers an alternative approach for deployment in spectrum monitoring networks for compliance enforcement in dynamic spectrum access networks.