Hybrid Coherent/Energy Detection for Cognitive Radio Networks

Abstract

Cognitive radio (CR) networks require reliable spectrum sensing techniques 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 usually also contain known symbol such as pilots and preambles for synchronization and channel estimation purposes. Coherent, correlation based spectrum sensing techniques can exploit these known symbols but waste the energy contained in the data symbols. In this paper, we propose a hybrid spectrum sensing scheme which exploits both the pilot and the data symbols transmitted by the primary user. For the practically relevant low signal-to-noise ratio regime, we derive a locally optimal hybrid detection metric, which turns out to be a linear combination of an energy detection metric and a correlation metric. Simulation and analytical results confirm that the hybrid metric outperforms both energy detection and coherent detection even if the pilot positions are not known a priori and have to be estimated by the CR receiver.