Evolutionary Optimization of Residual Neural Network Architectures for Modulation Classification
Evolutionary Optimization of Residual Neural Network Architectures for Modulation Classification
复制标题
DOI：
10.1109/tccn.2021.3137519
复制
发表时间：
2022-06-01
影响因子：
8.6
通讯作者：
Zheleva, Mariya
中科院分区：
文献类型：
--
作者：
Perenda, Erma;Rajendran, Sreeraj;Zheleva, Mariya
自动调制分类在当前和未来的无线通信系统的上下文中受到极大的关注。深度学习成为调制分类的强大工具，因为它允许联合判别特征学习和信号分类。然而，用于调制分类的深度神经网络架构的优化是一个手动且耗时的过程，需要深厚的领域知识和大量的工作。大多数最先进的解决方案主要集中在分类精度上，而忽略了网络复杂性的优化。本文提出了一种新的双目标模因算法BO-NSMA，用于搜索调制分类的最佳深度神经网络架构，以最大限度地提高分类精度并最大限度地降低网络复杂度。实验表明，BO-NSMA的初始种群为六个个体，仅经过十代，就找到了一种比所有人工构建的架构性能更好的深度神经网络架构。此外，BO-NSMA发现了第一个低复杂度卷积神经网络架构，其性能略优于昂贵的递归神经网络架构，使网络复杂度降低2.9倍，性能提高1.43%。与网络架构搜索的同行相比，BO-NSMA找到了最佳架构，实现了高达18.73%的准确性增益和高达82倍的网络复杂性降低。使用Wilcoxon符号秩检验验证结果。
Automatic modulation classification receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful tool for modulation classification, as it allows for joint discriminative features learning and signal classification. However, the optimization of deep neural network architectures for modulation classification is a manual and time-consuming process that requires profound domain knowledge and much effort. Most state-of-the-art solutions focus mainly on classification accuracy, while optimization of network complexity is neglected. This paper presents a novel bi-objective memetic algorithm, BO-NSMA, to search optimal deep neural network architectures for modulation classification to maximize classification accuracy and minimize network complexity. The experiments show that BO-NSMA, with an initial population of six individuals and only ten generations, finds a deep neural network architecture that outperforms all human-crafted architectures. Furthermore, BO-NSMA discovered the first low-complexity Convolutional neural network architecture, which achieves slightly better performance than costly Recurrent neural network architectures, allowing a 2.9-fold reduction in network complexity with 1.43% performance improvement. Compared to counterparts from network architecture search, BO-NSMA finds the best architecture, which achieves up to 18.73% accuracy gain and up to an 82-fold reduction in network complexity. The results are validated using the Wilcoxon signed-rank test.