Gamal Abdel Fadeel Mohammed-07
نویسنده
چکیده
It is becoming increasingly evident that cognitive radio (CR) users in CR networks acting in uncertain dynamical environments often employ exact or approximate Bayesian statistical calculations in order to continuously estimate the channel states. In this work we propose a prediction/filtering channel state estimation model capable of exactly implementing Bayesian state estimation and prediction from input point process measurements in real time. This setup is ideally suited to real CR networks. The results suggest that our model is useful for improving the performance of the sensing mechanism in practical CR environment.
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