Energy-Based Collaborative Spectrum Sensing for Cognitive UWB Impulse Radio

Abstract

This paper focuses on the issue of collaborative spectrum sensing in cognitive ultra wideband (CUWB) impulse radio. We employ energy-based signal detection method and apply the Neyman-Pearson (NP) decision rule to determine the optimum threshold. Two cooperative spectrum sensing schemes are developed in this paper. The decision fusion scheme is based on hard decision, in which each cooperating cognitive user (CU) sends its own local decision to the fusion center (FC). The FC then makes a final decision according to the majority voting rule. Alternatively, the data fusion scheme is based on soft decision, in which each local CU sends its observed value directly to the FC. The FC combines these values, compares to a threshold and then makes the final decision. The performances of both schemes are studied by using analytical tools and computer simulations. The receiver operating characteristics (ROC), which reveal the probability of detection versus false-alarm curve, are employed to evaluate the system performance under different scenarios. Simulation results demonstrate that the data fusion scheme outperforms the decision fusion scheme and verify that the collaborative spectrum sensing has practical importance in CUWB networks.