Robust Channel Invariant Deep Noncooperative Spectrum Sensing

Abstract

Deep learning (DL) has been introduced to cognitive radio network to solve the problem of spectrum scarcity and further enhance the spectrum utilization. However, many DL-based spectrum sensing methods are sensitive to the environment, which means the sensing model needs to be re-trained with a large number of labelled samples in a new environment. In this letter, we propose a novel DL-based channel environment-robust spectrum sensing network named ER-SNet, which contains the encoder part extracting channel invariant features and the classifier part for true hypothesis prediction. Extensive simulations have been conducted to show the performance improvement and robustness of the proposed algorithm in sensing weak signals over different channel conditions.