Cooperative spectrum sensing based on matrix rank minimization

In cognitive radio (CR) networks, multi-CR cooperation typically takes place during spectrum sensing, to cope with wire less fading effects and the hidden terminal problem. The user cooperation gain not only offers channel diversity against fading, but also allows for reduced sampling costs per CR, which is particularly relevant when the monitored spectrum has very wide bandwidth. To attain a desired tradeoff between channel diversity and sampling costs, this paper develops a new cooperative spectrum sensing technique based on matrix rank minimization. In the absence of channel knowledge, CRs individually collect digital measurements from a region of the wide spectrum via selective filtering, with optional compressive sampling to further reduce sampling rates. The solutions representing all the measurements are modeled to possess a low rank property, with the rank order being the same as the size of the nonzero support of the monitored wide spectrum. Accordingly, a nuclear norm minimization problem is formulated to jointly identify the nonzero support and hence the overall spectrum occupancy. Simulations show that the pro posed technique outperforms traditional averaging-based co operative schemes, given the same sampling costs.