Spectrum sensing based on adversarial transfer learning

Recently, deep learning (DL) based spectrum sensing (SS) has drawn much attention due to its better capacity of feature extraction and superb performance. However, the model robustness of the DL based scheme is limited by reason of the dynamic radio environment, leading to the floating of sensing performance. Motivated by this, adversarial transfer learning is applied to SS here, where the model is pre-trained at the central node firstly and fine-tuned at the local nodes. More specifically, a 2D dataset of the observed signal is constructed under various signal-to-noise-ratio (SNRs) and a convolution neural network (CNN) model is designed. Then a part of samples with various SNRs in the constructed dataset are employed to pre-train the proposed CNN model. After that, the pre-trained CNN model is distributed to local nodes with different SNRs and the pre-trained CNN model is fine-tuned. The proposed CNN model is pre-trained based on the samples under various SNRs, resulting in its stronger adaptability at the local node. The simulation experiments validate the effectiveness of the proposed scheme.