Joint Sparse Spectrum Reconstruction and Information Fusion via L1-Minimization

This paper considers the problem of sensing a sparsely occupied wideband spectrum utilizing a set of geographically distributed sensing nodes as well as a fusion center. Exchange of measurement data between the sensing nodes and the fusion center takes up parts of the precious radio spectrum and thus, methods for diminishing the minimum amount of measurements still ensuring a reliable reconstruction of the spectrum at the fusion center are needed. To this end we propose two approaches in the form of convex optimization problems to tackle the problem. The first approach applies classic compressive sampling, while the second approach improves the optimization problem, so that all measurements which have been acquired in a distributed manner can be taken into consideration in a single spectrum recovery operation. This makes it possible, to exploit the inherent diversity gain. The presented approaches to efficient distributed spectrum sensing enable reliable dynamic spectrum access.