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 (CR). 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. In this paper, we develop soft-input sequence detection algorithms of Markov sources in noise for spectrum sensing in DSA networks. We assign different Bayesian cost factors for missed detections and false alarms, and we show that a suitably modified Forward-Backward sequence detection algorithm is optimal in minimizing the detection risk. Along the way, we observe new fundamental limitations that we call "Risk Floor" and "Limiting ROC" for energy detection and coherent detection due to the PU's spectrum access pattern.