Sensor Clustering and Sensing Technology for Optimal Throughput of Sensor-Aided Cognitive Radio Networks Supporting Multiple Licensed Channels

In cognitive radio networks, secondary users may not sense licensed channels efficiently due to problems of fading channel, shadowing, unfamiliar environment, and so forth. To cope with the limitation, a pervasive sensor network can cooperate with cognitive radio network, which is called sensor-aided cognitive radio network. In the paper, we investigate sensor clustering and sensing time of each sensor cluster with aiming at achieving optimal throughput of sensor-aided cognitive radio network supporting multiple licensed channels. Moreover, the minimum throughput requirement of cognitive radio user is also guaranteed in the sensor clustering problem. To do this, we formulate the throughput maximization problem as a mixed-integer nonlinear programming and utilize the Branch and Bound algorithm to solve it. We also propose an heuristic algorithm which can provide similar performance to that of the Branch and Bound algorithm while reducing computation complexity significantly.