Sampling Spectrum Occupancy Data Over Random Fields: A Matrix Completion Approach
The performance of cognitive radio networks is fundamentally determined by the availability of spectrum re-sources. Detailed measurement campaigns are needed to collect the spectrum occupancy data to obtain a deeper understanding of the spectrum usage characteristics in cognitive radio networks. This approach, however, is usually inefficient due to the ignorance of the spatial, temporal and spectral correlations of spectrum occupancies, and unpractical because of the geographical and hardware limitations of the cognitive radio nodes. In this paper, the authors apply the theory of random fields to model the spatial-temporal correlated spectrum usage data, using the two dimensional Ising model and the Metropolis-Hastings algorithm respectively.