Implementation-Oriented Model for Centralized Data-Fusion Cooperative Spectrum Sensing

We propose a new model for a cognitive radio in the scenario of centralized data-fusion cooperative spectrum sensing. The model is grounded on a direct-conversion receiver architecture and was applied to four detection methods: the eigenvalue-based generalized likelihood ratio test, the maximum-minimum eigenvalue detection, the maximum eigenvalue detection and the energy detection. It is shown that the sensing performance under the conventional model typically adopted in the above scenario is overestimated when compared with the proposed one, which suggests that our model better suits for the spectrum sensing design and its performance assessment.