Abstract
Automatic modulation classification (AMC) algorithms based on deep learning (DL) have been widely studied in the past decade, showing significant performance advantage compared to traditional ones. However, the existing DL methods generally behave worse in computational complexity. For this, this paper proposes a lightweight convolutional neural network (CNN) for AMC task, where we design a depthwise separable convolution (DSC) residual architecture for feature extraction to prevent the vanishing gradient problem and lighten the computational burden. Besides that, in order to further reduce model complexity, global depthwise convolution (GDWConv) is adopted for feature reconstruction after the last (non-global) convolutional layer. Compared to recent works, the experimental results show that the proposed network can save approximately 70~98% model parameters and 30~99% inference time on two well-known benchmarks.
Highlights
Considering the aforementioned problems of deep learning (DL)-based methods in Automatic modulation classification (AMC), in this work, we propose an efficient and lightweight convolutional neural network (CNN) architecture, namely LWAMCNet
We find that: (1) VGG network presents the worst accuracy due to its relatively simpler structure and the usage of less convolution layers; (2) modulation classification convolutional neural network (MCNet) behaves best when signal-to-noise ratio (SNR) is less than 0 dB; converges to relatively worse point at high SNRs; and (3) LWAMCNet achieves the best at higher SNRs, with an improvement of 0.42 to 7.55% at 20 dB compared to the others
A residual architecture is designed by depthwise separable convolution (DSC) for feature extraction, which can significantly reduce the computational complexity of the model
Summary
Automatic modulation classification (AMC) is a vital technology between signal detection and demodulation in non-cooperative communication scenarios. AMC means to non-cooperatively classify the modulation scheme of a received radio signal, which can be regarded as a multi-class decision problem. As the foundation of signal demodulation, the correctness of AMC directly determines whether valid information can be recovered from the received signal. Rapid and accurate AMC of wireless signals is widely applied in various civilian and military fields, such as spectrum monitoring, radio fault detection, automatic receiver configuration, and signal interception and jamming [1–3]. Traditional AMC methods can be divided into two categories: likelihood based [4]
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