Adaptive threshold architecture for spectrum sensing in public safety radio channels

Cognitive radio make use of spectrum sensing techniques to detect licensed users transmissions and avoid causing interference. The major drawback in current spectrum sensing techniques is the use of static decision thresholds to detect such transmissions, which may be infeasible in public safety radio channels. More precisely, the cognitive radio may find different noise or interference levels when switching among these channels. This can lead to a wrong picture of the channel occupancy status, which in turn can increase the interference caused to licensed users. In this paper we propose an Adaptive Threshold Architecture, which uses machine learning algorithms to dynamically adapt the decision threshold, enabling the detection of licensed users transmissions in public safety radio channels. Results showed that the proposed architecture increased the sensing accuracy up to 2 times, providing results up to 6 times faster when compared to other solutions of the literature.