Alpha稳定分布噪声下基于轻量级网络的自动调制识别技术
Automatic Modulation Recognition Based on Lightweight Network Under Alpha Stable Distributed Noise
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摘要: 自动调制识别在无线通信领域发挥了巨大作用。多数研究中假设的加性高斯白噪声信道已不再满足实际信道环境的准确描述。实际中，由于闪电、雷暴、多用户干扰、设备故障等原因，信道环境中广泛存在Alpha稳定分布噪声。因此对其开展研究更符合实际且具有挑战性。该文针对Alpha稳定分布噪声提出了一种预处理联合轻量级网络的调制识别方法。首先，通过对数域映射及阈值限制对接收信号进行预处理，抑制由Alpha稳定分布噪声带来的尖锐脉冲将信号幅度控制到合理范围；然后，提出一种基于Ghost模块的轻量级网络来完成信号的调制识别分类任务。实验结果表明，与现有的CLDNN（Convolutional Long Short-term Deep Neural Network）、CNN（Convolutional Neural Network）、ResNet（Residual Network）相比，本文所提方法具有较高的识别准确率及较低的计算复杂度。Abstract: Automatic modulation recognition has played a huge role in wireless communication. Most studies hypothesis additive Gaussian noise channels no longer meet the accurate description of the actual channel environment. In practice， Alpha stable distribution noise is widely presented due to lightning， thunderstorm， multi-user interference， equipment failure， etc. Therefore， it is more in line with the actual and challenging. This article proposes a modulation recognition method for preprocessing a combined lightweight network for Alpha stable distribution noise. First， by pretreatment of the receiving signal to the digital domain mapping and threshold limit， the sharp pulse caused by the Alpha stable distribution noise is controlled to a reasonable range； then， a lightweight network based on the Ghost module is proposed. Complete the signal’s modulation identification classification task. The experimental results show that the method mentioned in this paper has high recognition accuracy and lower computational complexity compared to existing CLDNN， CNN， ResNet.
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