Sequence Detection Algorithms for Dynamic Spectrum Access Networks
Spectrum sensing is a critical function for enabling Dynamic Spectrum Access (DSA) in wireless networks that utilize cognitive radio. In DSA networks, unlicensed secondary users can gain access to a licensed spectrum band as long as they do not interfere with primary users. Spectrum sensing is subject to errors in the form of false alarms and missed detections. False alarms cause spectrum under-use by secondary users, and missed detections cause interference to primary users. Although existing research has demonstrated the utility of a Markov chain for modeling the spectrum access pattern of primary users over time, little effort has been directed toward spectrum sensing based upon such models.