Hybrid Data Augmentation and Dual-Stream Spatiotemporal Fusion Neural Network for Automatic Modulation Classification in Drone Communications

Abstract

Automatic modulation classification (AMC) is one of the most important technologies in various communication systems, including drone communications. It can be applied to confirm the legitimacy of access devices, help drone systems better identify and track signals from other communication devices, and prevent drone interference to ensure the safety and reliability of communication. However, the classification performance of previously proposed AMC approaches still needs to be improved. In this study, a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC approach is proposed to enhance the classification accuracy for the purpose of aiding drone communication because SDDFNN can effectively mine spatiotemporal features from modulation signals through residual modules, long-short term memory (LSTM) modules, and attention mechanisms. In addition, a novel hybrid data augmentation method based on phase shift and self-perturbation is introduced to further improve performance and avoid overfitting. The experimental results demonstrate that the proposed AMC approach can achieve an average classification accuracy of 63.44%, and the maximum accuracy can reach 95.01% at SNR = 10 dB, which outperforms the previously proposed methods.

MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

“…To solve the domain mismatch problem of deep learning structures, Zhang et al [11] introduced a neural architecture search (NAS)-based AMC framework that automatically adjusts the connection and parameters of DNNs to find the optimal structure under a combination of training and constraints. In [13], a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC method was developed to improve accuracy in order to assist drone communications since the DSSFNN is able to efficiently mine spatiotemporal features from modulated signals through residual blocks, LSTM blocks, and attention mechanisms. In [27], Zheng et al proposed two-stage data augmentation (TSDA) based on spectral interference in deep learning to improve the AMC generalization ability across different communication scenarios.…”

“…In this part, a series of state-of-the-art methods is compared to prove the superiority of the proposed method, and the experimental results of two datasets are reported in Tables 3 and 4. The experimental results for the larger dataset, RadioML 2018.01A, are mainly observed to determine the AMC accuracy by comparing it with HCGDNN [5], DSSFNN [13], Deep-LSTM [29], and GCN [31], while the smaller dataset, RadioML 2016.10A, is tested to comprehensively evaluate the overall AMC performance by comparing it with Augmented CNN [27], DL-PR: CNN [30], MS-Transformer [32], and FC-MLP [46]. According to the results, the pruned models, i.e., MobileRaT-A (0.67×) and MobileRaT-B (0.38×), improve their respective AMC accuracies from 60.2% and 62.6% to 61.8% and 63.2%, accompanied by more efficient reasoning.…”

“…Specifically, channel conditions [12] cause the attenuation and distortion of signals during transmission. Multipath propagation and Doppler shifts [13] lead to a delay in spreading signals. Interference from other wireless devices and noises in the environment has a negative impact on the recognition of signal modulation methods.…”

An Ultra Lightweight Neural Network for Automatic Modulation Classification in Drone Communications

“…Phase shift 24 is an effective means to achieve data augmentation in radio signal processing. By rotating the radio signal, we can generate data samples with different phase angles without altering the signal amplitude, thereby increasing the diversity of the dataset.…”

Overview of Data Augmentation Techniques in Time Series Analysis

“…• Hybrid data augmentation approaches that combine statistical, machine learning, and deep learning methods offer a promising avenue for exploration [100].…”