An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks

摘要

Spectrum sensing methods in Cognitive Radio have been one of the key areas of research. To maximize the Probability of Detection for a given Probability of False Alarm in varying environmental conditions has been a challenging task. Making the decision threshold adaptive to the channel conditions is one of the ideas that become an optimal solution for the same. Here we propose an Adaptive Threshold scheme for Matched Filter based detection over an Additive White Gaussian Noise channel by implementing Artificial Neural Networks. Predictive analysis and experiential learning become viable components in achieving high performance even during extreme fading conditions.