Traffic Signal Detection
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Abstract—Traffic Signal Detection. Due to random arrivals of primary user signals, their timing misalignment issue should be considered for spectrum sensing in cognitive radio (CR) systems, such as CR based femtocell networks. To deal with this issue in the literature, two approaches were recommended, including Bayesian and generalized likelihood ratio test (GLRT) detectors. However, Bayesian test requires the perfect knowledge of the distribution of unknown parameters. Therefore, < Final Year Projects > it is considered to be impractical due to its implementation complexity. To design a low complexity energy detector (ED), this work proposes an ED scheme based on GLRT algorithm. As a result, maximum-likelihood (ML) estimation for the timing misalignment is devised, and the performance of the proposed scheme is analyzed. The results show that the proposed GLRT detector features a low complexity and satisfactory performance.
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