A Robust Cooperative Spectrum Sensing Method Based on Reliable Evaluation Criteria

Abstract

The present invention proposes a reliable evaluation criterion-based robust cooperative spectrum sensing method. According to the method, channel access feedback and full-time slot sensing are introduced to establish a reliable evaluation standard; based on the evaluation standard, the idle probability and occupation probability of an authorized channel and the detection probability and false alarm probability of a sensing node are calculated according to data in first N time slots; and local judgment results reported by the sensing node at the current time slot are fused according to the above calculation results, so that global judgment is performed. The method of the invention has high robustness and can maintain good performance under a large number of malicious users.

Description

technical field [0001] The invention belongs to the field of cognitive radio in wireless communication technology, and in particular relates to a robust cooperative spectrum sensing method based on a reliable evaluation standard. Background technique [0002] With the explosion of wireless devices, the problem of spectrum shortage has become more prominent. Among many solutions, cognitive radio is widely regarded as an effective means to improve spectrum utilization and solve spectrum shortage. Cognitive radio is an intelligent wireless communication system capable of sensing the environment, learning from it, and adaptively adjusting input parameters to adapt to changes. Spectrum sensing is the key technology for sensing the environment. Spectrum sensing is used to discover spectrum holes of licensed channels and sense the existence of licensed users on channels, so that sensing users can use spectrum holes in time without interfering with normal communication of licensed...

AI Technical Summary

Problems solved by technology

The former evaluation standard is based on the assumption that honest nodes account for the main body. Even when there are fewer honest nodes, the influence of malicious nodes on the evaluation standard cannot be ignored; the latter evaluation standard assumes that there are one or more known honest nodes. nodes, and establish evaluation criteria based on the reported data of these nodes. Although the influence of malicious nodes is avoided, it is difficult to obtain the prior information of honest nodes. Moreover, these nodes may suffer from severe shadow weakening, and the reported data is not reliable.

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