Research On Energy - Saving Perception, MAC Protocol Identification And Modulation Recognition Algorithm In Cognitive Sensor Networks

Recently, with the development of wireless communication technology, a variety of wireless communication systems have emerged, and cognitive radio sensor networks (CRSN) have attracted increasing attention. It can not only alleviate excessive congestion of the free band over which traditional sensor networks operate, but also improve the utilization of licensed spectrum. However, the measure of selecting cognitive sensors for energy-efficient spectrum sensing, and the methods to identify MAC protocol and modulation mode used in the current primary network are still problems to be solved in CRSN. This dissertation focuses on the mentioned problems above, and obtains the following research results:(1) Aiming at saving energy of cognitive sensors on spectrum sensing, a cluster and slot based energy-efficient spectrum sensing algorithm is proposed. The energy consumption of spectrum sensing and data communication is analyzed to determine the optimal number of clusters and nodes for cooperative sensing. Besides, the optimal sensing time slot interval of cognitive sensors is studied. Simulation results show that this algorithm reduces total energy consumption, as well as guarantees the reliability of spectrum sensing.(2) In order to make efficiently use of spectrum holes, the scheme of popular MAC protocols are analyzed. The signal power and channel occupancy time are extracted as features used by cognitve users to identify MAC protocol of an unknown primary network by support vector machine classifiers. Simulation results show that the correction rate of this classification algorithm increases with the increasing network load.(3) For the purpose of adjusting transmission parameters of cognitive sensors according to external radio environment, the fourth order cumulants of five kinds of commonly used modulated signals are analyzed. A novel modulation classification algorithm is presented based on the joint probability distribution of fourth order cumulants. The weighting factors are introduced to raise the classification rate. Simulation results show that the algorithm can effectively identify the modulation of transmitting signals.Therefore, algorithms proposed in this dissertation can effectively reduce the network energy consumption and make efficiently use of licensed spectrum holes.