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Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

机译：基于深度学习的自动调制识别方法在存在相位偏移中

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摘要

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in realistic communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.
机译：自动调制识别（AMR）在各种通信系统中起重要作用。它具有自适应调制的能力，可以适应各种复杂环境。自动调制识别也广泛用于正交频分复用（OFDM）系统。但是，由于传统方法的识别准确性提取OFDM信号的特征非常有限。为了解决这些问题，已经提出了许多基于深度学习的AMR方法来提高识别性能。然而，大多数这些AMR方法忽视了载体相位偏移（PO）的有害效果，该抵消（PO）经常出现在现实通信系统中。因此，需要考虑设计OFDM系统的PO效果。与传统方法不同，我们提出了一种基于卷积神经网络（CNN）的AMR方法，用于考虑OFDM系统。所提出的方法用于消除PO以实现高分类精度。提供实验结果以确认与常规方法相比的提出的方法。

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