Cognitive radio (CR) networks require reliable spectrum sensing in order to avoid interference to the primary users of the spectrum. Most existing spectrum sensing techniques are based on simple energy detection. However, in practice, the signals transmitted by primary users often also contain known pilot symbols for synchronization and channel estimation purposes. Coherent correlation based spectrum sensing techniques can exploit these known symbols but fail to utilize the energy contained in the data symbols. In this paper, we propose a hybrid coherent/energy detection scheme for spectrum sensing which exploits both the pilot and the data symbols transmitted by the primary user. Since the complexity of the globally optimal hybrid detection metric is very high, we develop a simple locally optimal hybrid metric, which turns out to be a linear combination of an energy detection metric and a correlation metric. We derive the probabilities of false alarm and missed detection of the proposed hybrid detector, and investigate the asymptotic behavior of both quantities for low signal-to-noise ratio and large sample size assuming that the CR network is optimized using a Neyman-Pearson framework. Simulation and analytical results confirm that the hybrid metric outperforms both energy detection and coherent detection even if the positions of the pilot symbols are not known a priori.
['Farzad Moghimi', 'Robert Schober', 'Ranjan K. Mallik']
Hybrid Coherent/Energy Detection for Cognitive Radio Networks