A Low-Complexity Sequential Spectrum Sensing Algorithm for Cognitive Radio

Yan Xin, Honghai Zhang, Lifeng Lai

Abstract: In this paper, we propose a sequential spectrum sensing algorithm for cognitive radio systems, which we term the sequential shifted chi-square test (SSCT). SSCT has the following attractive features for practical implementations. First, SSCT employs a simple test statistic and thus has a low implementation complexity. Secondly, SSCT is a sequential detection algorithm and is capable of achieving performance comparable to fixed sample size detection algorithms such as energy detection but with much reduced sensing time. Thirdly, SSCT is essentially a non-coherent detection algorithm in the sense that it does not require any deterministic knowledge of the primary signals. Lastly, SSCT is able to strike a desirable trade-off between sensing performance and sensing time particularly in the signal-to-noise ratio mismatched case. To evaluate sensing performance, we derive the exact false-alarm probability for SSCT, and develop numerical integration algorithms to compute misdetection probability and the average sample number. We further demonstrate the performance of SSCT with several numerical examples.