A unified framework for robust cooperative spectrum sensing

Cooperative spectrum sensing is a key component in cognitive radio and cognitive radio network based applications. Like any other network, the spectrum sensing performance may be degraded by various sensor faults and/or security threats. These security issues can be grouped into mainly two categories: ones that do not depend on the channel state, such as device malfunctions due to hardware/software failures, and ones that depend on the channel state such as Byzantine attacks. In this paper, we propose a robust spectrum sensing framework including two steps of faulty node detection followed by faulty node elimination or correction before decision fusion. The first step explores the decision statistics over time to identify the potentially faulty nodes based on the Sanov's theorem. The second step relies on the mutual behavior check among the remaining nodes for detection. Specifically, the minimum description length principle is explored for clustering decision sequences from different cognitive radio nodes. Maximum likelihood estimation is used for faulty model parameter estimation, which is then used for data correction. Simulations are conducted for different window sizes, node numbers, and quality of the network to demonstrate the effectiveness of the proposed framework.