Research On Energy-efficient Cooperative Spectrum Sensing Technology Based On Wireless Sensor Networks

In recent years,a large number of new wireless communication technology has made the already very scarce wireless spectrum resources become more scarce.Research shows that a considerable portion of the wireless spectrum resource is not fully utilized,whether in time dimension or space dimension.Cognitive radio technology can solve the contradiction between the lack of wireless spectrum resource and the low spectrum utilization by allowing the secondary user to access the spectrum of primary users and adopting dynamic spectrum access technology.Spectrum sensing is one of the key technologies of cognitive radio.Collaborative spectrum sensing technology uses multiple nodes simultaneously spectrum sensing,can greatly improve the accuracy and robustness of spectrum sensing.Peeling the spectrum sensing function from the cognitive terminal can reduce the complexity of the cognitive terminal device,reduce the energy consumption of the cognitive terminal,and enhance the reliability of the sensing result.For this purpose,a new cognitive radio network framework-the sensor-aided cognitive radio network framework was proposed.In the sensor-aided cognitive radio network,spectrum sensing is performed by an independent wireless sensor network,and the sensing result is reported to the cognitive user network.Wireless sensor networks are energy-constrained networks that require energyefficient scheduling algorithms to improve network performance.The existing energyefficient sensor scheduling problem only considered single frequency band.Multi-band energy-efficient sensor scheduling problem has many novel research fields.We formulate a multi-band sensor collaborative spectrum sensing model and propose two energyefficient scheduling algorithms to improve the throughput.We consider the energy consumption of switching frequency band.In this thesis,the cognitive base station assigns a set of sensors for each frequency with the aim of improving energy efficiency for collaborative spectrum sensing.Our algorithms optimally schedule the activities of the sensors to increase the overall secondary system throughput.Simulation results show that our algorithms achieve higher network throughput and energy efficiency than greedy algorithm and the algorithm proposed in prior paper.