Spectrum Sensing Using Hidden Markov Modeling

Cognitive radio technologies are being developed which allow heterogeneous systems to share spectrum access while minimizing interference to improve the overall efficiency of spectrum usage. Interference minimization requires cognitive radio receivers to be able to detect the presence of all other systems competing for spectrum usage, a process often termed "spectrum sensing". This paper focuses on the kernel function of spectrum sensing: blind interference detection from a single, strictly time-limited, received data vector. Recent research has identified shortcomings in the operation of classical blind interference detection techniques such as energy detection and radiometry. This paper demonstrates that implicit interference characteristics can be exploited in a formal framework using hidden Markov modeling to produce a spectrum sensor with a receiver operating characteristic which is improved on that of energy detection and several other previously reported methods.