Collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks

Abstract

Abstract-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%.

Dynamic compressive spectrum sensing for cognitive radio networks

“…The algorithm in [4] takes into consideration the large dynamic range of channel powers so it returns more stable recovery.…”

“…However, each CR has a limited reach, and channel scan is time consuming. Inspired by compressive sensing [3], a collaborative spectrum sensing method was proposed in [4] to resolve these problems. Instead of sweeping channels sequentially, each CR is equipped with a few frequency selective filters, through which a number of linear combinations of the powers of all channels are recorded and then sent to the fusion center (FC).…”

“…This set of jointly sparse power vectors are recovered to reveal the occupied channels. The work [4] allows the communications between the CRs and FC to be lossy. Missing or erroneous reports can be accurately recovered by matrix completion (see [6], [9]) since the matrix formed by all reports has a low rank.…”

“…However, the work [4] only discusses spectrum sensing in a static environment. In the real world, PRs may start or stop using their channels at any time.…”

BCS: Compressive sensing for binary sparse signals

“…Binary sparse signals come into account in many practical applications such as event detection in wireless sensor networks, group testing, spectrum hole detection for cognitive radios, etc [5]. Linear programming based solution to (3) have already been proposed and discussed.…”