Collaborative Spectrum Sensing From Sparse Observations Using Matrix Completion for Cognitive Radio Networks
Source: University of Houston
In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, the authors apply recent matrix completion techniques to greatly reduce the sensing information needed. They formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem.