Creating Spatio-temporal Spectrum Maps from Sparse Crowdsensed Data

Shared spectrum systems is an emerging paradigm to improve spectrum utilization and thus address the unabated increase in mobile data consumption. The paradigm allows the 'unused' spectrum bands of licensed Primary Users (PUs) to be shared with Secondary Users (SUs), without causing any harmful interference to the PUs. Allocation of spectrum to the SUs is done based on spectrum availability at the SUs' locations; such allocation of spectrum is greatly facilitated by spectrum occupancy maps. In this work, we address the problem of creating spectrum occupancy maps from spectrum occupancy data over a large number of instants, in the challenging scenario of dynamically (temporally) changing spectrum occupancy due to intermittent transmission of primary users. The problem is particularly challenging when the available occupancy data is very sparse spatially, i.e., only very few locations report sensing data at any particular instant. We design various techniques to create spectrum maps in the above context, including a promising correlation-based merging method that merges observation vectors iteratively in conjunction with careful interpolation. Using extensive simulation over data including real data from cellular and deployed WiFi settings, we show that the correlation-based method is very effective in generating high-accuracy spatiotemporal spectrum maps even with very sparse observation vectors (as long as the number of such vectors is large enough).