Adaptive modem and interference suppression based on deep learning

AbstractAbstractWith the increasingly fierce competition of electromagnetic spectrum, developing intelligent communication systems that can reconfigure its waveform can effectively improve the communication system's ability to adapt to the complex electromagnetic environment. In this paper, an adaptive modem based on a deep autoencoder network (DAN) is designed, and the demodulation performance that is close to, consistent with, or better than traditional MPSK or QAM is achieved. This DAN can be trained by a unified loss function and a unified optimization algorithm to implement a variety of modems. For high‐order modems, the constellation diagrams generated by the DAN are radically different from the traditional modulation and very difficult to distinguish linearly, which is beneficial to improve the anti‐interception ability of the communication systems. Additionally, based on the trained DAN, a convolutional neural network (CNN) with a single convolutional layer is incorporated to suppress a variety of interferences. By using transfer learning, the new deep learning (DL) model with the CNN converges fast with a few epochs of training. Training and test results verify that the new DL model can improve the anti‐interference ability of the communication systems by learning and suppressing the interferences in both frequency and power domains.An adaptive modem is proposed to generate constellation diagrams that can resist malicious interferences by learning and suppressing the interferences in both frequency and power domains. View Figure