Efficient utilisation and sharing of limited spectrum resources in an autonomous fashion is one of the primary goals of cognitive radio. However, decentralised spectrum sharing can lead to interference scenarios that must be detected and characterised to help achieve the other goal of cognitive radio—reliable service for the end user. Interference events can be treated as unusual and therefore anomaly detection algorithms can be applied for their detection. Two complementary algorithms based on information theoretic measures of statistical distribution divergence and information content are proposed. The first method is applicable to signals with periodic structures and is based on the analysis of Kullback-Leibler divergence. The second utilises information content analysis to detect unusual events. Results from software and hardware implementations show that the proposed algorithms are effective, simple, and capable of processing high-speed signals in real time. Additionally, neither of the algorithms require demodulation of the signal.
The Information Theoretic Approach to Signal Anomaly Detection for Cognitive Radio
