Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

Ke, Ziqi; Vikalo, Haris

doi:10.48550/arxiv.2011.08295

Cited by 1 publication

(1 citation statement)

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“…[31] applied GRU for AMC with resource-constrained end-devices. Utrilla [32] designed a LSTMbased denoising autoencoder classifier and exceeded 90% for an SNR of 4 dB. West [33] utilized Inception structure [34] and Residual module to extract features and can reach 80% accuracy for an SNR of 0 dB.…”

Section: Introductionmentioning

confidence: 99%

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

Zhang

Yin

et al. 2021

Applied Sciences

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Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

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