  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, we apply recent matrix completion techniques to greatly reduce the
sensing information needed. We formulate the collaborative sensing problem as a
matrix completion subproblem and a joint-sparsity reconstruction subproblem.
Results of numerical simulations that validated the effectiveness and
robustness of the proposed approach are presented. In particular, in noiseless
cases, when number of primary user is small, exact detection was obtained with
no more than 8% of the complete sensing information, whilst as number of
primary user increases, to achieve a detection rate of 95.55%, the required
information percentage was merely 16.8%.
