Spectrum Sensing Based On Cognitive Radio Technology And The Positioning Of Interference Source

With the rapid development of wireless communication, the supply and demand of spectrum resources become unbalanced gradually. Avoiding the communication interference taken from the cognitive user to the main user (Primary User, PU) is the most important cognitive limitations, and the key technologies to achieve this goal is spectrum sensing. Number of nodes in CR can be used for collaborative spectrum sensing, cooperative spectrum sensing can effectively overcome the limitations of a single node spectrum sensing and improve the performance of spectrum sensing. This paper propose an improved method of collaborative spectrum sensing based on the detected energy , and conduct location analysis of the cognitive users which take interference to primary users in real world.Firstly, this paper introduces the classification of spectrum testing, describe various testing methods, and give an overview of their advantages and disadvantages.Then introduce the spectrum of collaborative detection performance in detail and the hard decision based on data fusion of collaborative detection and energy detection of soft decision synergy testing and integration of soft and hard decision of data fusion. In the end propose integration of collaborative spectrum sensing detection at different SNR.Secondly, introduce the basic principles of smart antenna, analyse the feasibility that smart antenna used to cognitive radio, and make sure of multiple signal classification algorithm can be used to locate interference sources. Then introduce the traditional smart antenna algorithms and rotation invariant subspace algorithm.Finally, introduce multiple signal classification algorithms, analyse the model of coherent signal source of the actual environment, Based on the MUSIC algorithm , proposed an improved method of the beam-space multiple signal classification algorithm, to locate cognitive users that interferenced by cognitive radio. Through MATLAB simulation confirmed this algorithm can carry on the correct localization and accurately to the disturbance supply oscillator. The localization effect is good.