Data Fusion-based Cooperative Spectrum Sensing Technology Research

With the increasingly development of wireless application, more and more spectrum resources are needed to support numerous emerging wireless service, the spectrum resources are becoming more and more stringent. Cognitive Radios(CR) is an enabling technology that improves the utilization of scarce radio spectrum resources, which can sense the exterior environment, learn from exterior environment and adjust its inner parameters. Since cognitive Radios are considered as lower priority and they are secondary users of spectrum allocated to the primary user, a fundamental requirement is to avoid interference to potential primary users in their vicinity. Therefore, secondary users should be able to independently detect the presence of primary users through continuous spectrum sensing. Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem for cognitive radio. AS a result, spectrum sensing, especially cooperative spectrum sensing is attracting more and more attention in cognitive radio networks.The thesis mainly researches the techniques of wireless environment cognition and cooperative spectrum sensing. The performance of cooperative spectrum sensing depends on the reliability of local spectrum sensing result of cognitive nodes. In this paper, we think that different cognitive nodes'SNR leads to different reliability of local sensing results. Therefore, a cooperative spectrum sensing algorithm based on SNR comparison in fusion center is proposed, first, each CR transmits local detection result and SNR to the fusion center, then the SNR comparison is operated in the fusion center, finally, according to the proposed algorithm, the CR with better SNR are selected to decision fusion. The subject background is described in the first section, and the significance of CR is showed there. Then the spectrum sensing methods in CRN is discussed by theory and simulation following the description of spectrum sensing. Next, a new spectrum sensing algorithm proposed in this paper will be discussed. Finally, though simulations, the performances are examined and we further investigate the relationship between the nodes based on SNR and performances of the whole cognitive radio network.