Automatic Modulation Classification Using Deep Residual Neural Network with Masked Modeling for Wireless Communications

Abstract

Automatic modulation classification (AMC) is a signal processing technology used to identify the modulation type of unknown signals without prior information such as modulation parameters for drone communications. In recent years, deep learning (DL) has been widely used in AMC methods due to its powerful feature extraction ability. The significant performance of DL-based AMC methods is highly dependent on large amount of data. However, with the increasingly complex signal environment and the emergence of new signals, several recognition tasks have difficulty obtaining sufficient high-quality signals. To address this problem, we propose an AMC method based on a deep residual neural network with masked modeling (DRMM). Specifically, masked modeling is adopted to improve the performance of a deep neural network with limited signal samples. Both complex-valued and real-valued residual neural networks (ResNet) play an important role in extracting signal features for identification. Several typical experiments are conducted to evaluate our proposed DRMM-based AMC method on the RadioML 2016.10A dataset and a simulated dataset, and comparison experiments with existing AMC methods are also conducted. The simulation results illustrate that our proposed DRMM-based AMC method achieves better performance in the case of limited signal samples with low signal-to-noise ratio (SNR) than other existing methods.

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

“…Based on I/Q-driven outputs from different layers of the model, Chang et al [5] proposed a hierarchical classification head-based convolutional gated deep neural network (HCGDNN) consisting of three groups of CNN, two groups of bi-directionally gated recursive units (BiGRUs), and a hierarchical classification head. Peng et al [9] designed a deep residual neural network (DRMM) based on masked modeling to improve the AMC accuracy of deep learning with limited signal samples. Shen et al [10] developed a multi-subsampling self-attention (MSSA) network for drone-to-ground AMC systems, for which a residual dilated module incorporating both ordinary and dilated convolutions is devised to expand the data-processing range.…”

“…By utilizing AMC, drone communication systems extend the battery life of drones by selecting the appropriate transmission power based on channel conditions, transmission distance, and mission requirements [8]. Moreover, AMC possesses the ability to support more communication applications and functions by seamlessly integrating them into drone communication systems, enhancing their flexibility and scalability [9]. In fact, AMC has proven to be a key contributor to drone communications by optimizing modulation schemes to improve signal quality and anti-interference capabilities, ensuring that drone communication systems can operate stably and efficiently in various environments and application scenarios.…”

VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems

“…Initially, the modulation format recognition technique rapidly developed in the field of radio communications [5][6][7]. In recent years, numerous machine learning algorithms have also made strides in the field of MFR in optical communications [8][9][10][11][12][13].…”

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

“…In UAV communication systems, the application of AMC enables UAVs to adaptively select the most suitable modulation method based on current channel conditions and transmission distance, thereby effectively enhancing data transmission 2 . Furthermore, as a key technology 3 for dynamic spectrum access (DSA), AMC plays a significant role in enhancing spectrum efficiency and expanding communication capacity 4 . In complex electromagnetic environments 5 , AMC technology further demonstrates its potential in monitoring unauthorized devices or signals, particularly in applications such as UAV identification 6 , interference recognition 7 , and electronic countermeasures 8 .…”