Ensemble Deep Learning Based Cooperative Spectrum Sensing with Stacking Fusion Center

In this paper, an ensemble learning (EL) framework is adopted for cooperative spectrum sensing (CSS) in an orthogonal frequency division multiplexing (OFDM) signal based cognitive radio system. Each secondary user (SU) is accordingly considered as a base learner, where the local spectrum sensing is for investigating the probability of PU being inactive or active. The convolution neural networks with simple architecture are applied given its strength in image recognition as well as the limited computation ability of each SU, meanwhile, the cyclic spectral correlation feature is introduced as the input data. Here, as for the supervised learning, the bagging strategy is helped to establish the training database. For the global decision, the fusion center employs the stacked generalization for further combination learning the SU output of classification pre-prediction of the PU status. Our method shows significant advantages over conventional CSS methods in term of the detection probability or false alarm probability performance.