In cognitive radio, spectrum sensing is a challenging task. In this letter, a new spectrum sensing method is proposed based on Goodness of Fit test (GoF) of the energy of the received samples with a chi-square distribution. We derive the test statistic and evaluate the performance of the proposed method by Monte Carlo simulations. It is shown that our proposed spectrum sensing method outperforms the conventional energy detection (ED) without increasing the complexity of the sensing.

In this paper, the channel utilization (throughput vs sensing time relationship) is analyzed for cooperative spectrum sensing under different combining rules and scenarios. The combining rules considered in this study are the OR hard combining rule, AND the hard combining rule, the Equal Gain Soft combining rule and the two-bit quantized (softened hard) combining rule. For all combining rules, the detection performance, with a Gaussian distribution assumption, is expressed in two different scenarios, CPUP (Constant Primary User Protection) and CSUSU (Constant Secondary User Spectrum Usability). A comparison, based on simulations, is conducted between these proposed schemes in both scenarios, in terms of detection performance and throughput capacity of the CR network.

In this letter, a blind spectrum sensing method based on goodness-of-fit (GoF) test using likelihood ratio (LLR) is studied. In the proposed method, a chi-square distribution is used for GoF testing. The performance of the method is evaluated through Monte Carlo simulations. It is shown that the proposed spectrum sensing method outperforms the GoF test using Anderson Darling (AD) and the conventional energy detection (ED) in case of a low signal to noise ratio (SNR).

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