Collaborative sensing for cognitive radio under log-normal shadowing

Abstract

Spectrum sensing is an essential function in cognitive radio networks. It defines spectrum opportunities that can be used by secondary users to increase spectrum utilization. However, sensing performance is highly affected by shadowing. In this paper, sensing in a log-normal shadowing environment is investigated, where a closed-form for the probability of detection based on Gauss-Hermite integration is derived, and the accuracy of this form is studied. In addition, collaborative sensing in log-normal shadowing is addressed, where the performance of different soft combining schemes is evaluated via Complementary Receiver Operating Characteristic (CROC) curves.