Dynamic threshold adaptation for spectrum sensing in cognitive radio systems

In this paper, we investigate dynamic adaptation of the spectrum sensing threshold in cognitive radio systems. We use a filter bank approach for spectrum sensing, in which the available spectrum is divided into sub-bands, and obtain the optimal threshold values by minimizing the corresponding spectrum sensing error subject to constraints on the probabilities of missed detection and false alarms for each sub-bands. Furthermore, we propose a gradient descent based algorithm for threshold adaptation in dynamic scenarios, and provide numerical results obtained from simulations, these show how the proposed algorithm adjusts the threshold dynamically to minimize the spectrum sensing error subject to specified constraints.