Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication

•A method is proposed to analyze the radio spectral bias from fre- quency perspective, based on a multi-spectral attention mechanism with DCT for learning-based frequency components selection, termed as FLANs. This attention mechanism is the general case of conventional global aver- age pooling (GAP) and leverage identical structures of the popular neural networks.•We propose a simple yet effective learning-based dynamic frequency selection module in FLANs. The module consists of multiple learning-based switches to identify the trivial frequency components based on the DCT coefficients for static removal during inference.•To the best of our knowledge, this is the first work that compromises the merits of hand-crafted feature mechanism and deep learning in frequency domain for AMC. Extensive experiments have been conducted to validate the superiority of FLANs for automatic modulation classification over a wide variety of state-of-the-art methods on RADIOML 2018.01A dataset. Deep neural networks have been recently applied in automatic modulation classification task and achieved remarkable success. However, Existing neural networks mainly focus on the purely data-driven architecture design, and fail to explore the hand-crafted feature mechanisms which are particularly significant for radio signal presentation in wireless communication. Inspired by digital signal processing theories, we propose frequency learning attention networks (FLANs) to analyze the radio spectral bias from frequency perspective, based on a multi-spectral attention mechanism for learning-based frequency components selection. FLANs are the general case of classical global average pooling and leverage identical structures of the popular neural networks. Extensive experiments have been conducted to validate the superiority of FLANs for automatic modulation classification over a wide variety of state-of-the-art methods on RADIOML 2018.01A dataset.