National Digital Switching System Engineering and Technology R&D Center, Zhengzhou 450001, China.
Sensors (Basel). 2018 Nov 13;18(11):3913. doi: 10.3390/s18113913.
With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.
随着深度学习的飞速发展，自动调制识别技术也取得了迅猛的发展。大部分新提出的方法都依赖于大量的标记样本。我们致力于使用更少的标记样本，在认知无线电领域进行自动调制识别。在此，我们提出了一种基于对抗训练的半监督学习方法，称为信号分类器生成对抗网络。基于这项技术的大部分先前方法都涉及计算机视觉应用。然而，我们通过添加编码器网络和信号空间变换模块，改进了生成对抗网络的现有网络结构，使我们的框架能够更有效地处理无线电信号处理任务。这两项技术改进有效地避免了因无线电信号的复杂性而导致的不收敛和模式崩溃问题。仿真结果表明，与著名的深度学习方法相比，我们的方法在合成射频数据集上的分类准确率提高了 0.1%至 12%。此外，我们在半监督场景中验证了我们方法的优势，并与传统的半监督学习方法相比，获得了显著的准确性提升。
