Ensemble Learning Based Robust Cooperative Sensing in Full-Duplex Cognitive Radio Networks

We propose an ensemble learning (EL) based cooperative sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. The FD communication further improves the spectrum awareness capability of the secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. In the meantime, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification attacks also degrade the cooperative sensing performance in practice. To alleviate the influence of interference and attacks, we design an EL framework that provides robust and accurate fusion performance with low time cost. In such a context, we analyse the spectrum waste and collision probabilities in the FD Listen-And-Talk (LAT) protocol to measure the performance. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of much lower and stable spectrum waste and collision probability for any number of trusted SUs.