Distributed Sensing of a Slowly Time-Varying Sparse Spectrum Using Matrix Completion

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In this paper, the authors consider the problem of sensing a frequency spectrum in a distributed manner using as few measurements as possible while still guaranteeing a low detection error. To achieve this goal they use the newly developed technique of matrix completion which enables to recover a low rank matrix from a small subset of its entries. They model the sensed bandwidth at different cognitive radios as a spectrum matrix. It has been shown that in many cases the spectrum used by a primary user is underutilized. Therefore the spectrum matrix often has a low rank structure.