STARNet: An Efficient Spatiotemporal Feature Sharing Reconstructing Network for Automatic Modulation Classification

doi:10.1109/TWC.2024.3400754

STARNet: An Efficient Spatiotemporal Feature Sharing Reconstructing Network for Automatic Modulation Classification

2024-10-01

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DOI：10.1109/TWC.2024.3400754

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Automatic Modulation Classification (AMC) is a crucial task in the field of wireless communication, allowing for the identification of the modulation scheme of a received radio signal without prior knowledge of the communication system. Recently, AMC approaches based on Deep Learning (DL) have achieved outstanding results. However, the majority of current DL-based AMC methods face challenges in achieving high recognition accuracy while remaining computationally efficient. Some researchers have designed autoencoder-based models to generate low-dimensional temporal feature embeddings of the radio signal, thereby reducing the number of model parameters while maintaining high performance in recognizing modulation formats. However, when further improving AMC performance via learning low-dimensional spatial-temporal feature representations, traditional autoencoder models require both a convolutional decoder and an LSTM decoder to reconstruct temporal and spatial features separately, which unavoidably raises the model parameters. In this paper, we propose a spatiotemporal feature sharing reconstructing network (STARNet) to simultaneously extract low-dimensional spatial and temporal feature representations of radio signals using a single autoencoder structure, thereby reducing the number of model parameters and improving AMC performance. Additionally, we construct a Hybrid Attentive Ghost (HA-Ghost) to automatically extract discriminative radio signal spatial information according to signal reconstruction performance. Extensive experiments on benchmark datasets demonstrate that the proposed STARNet achieves an average modulation classification accuracy of 63.64%, outperforming previous state-of-the-art models. Despite extracting more types of features, STARNet has only 14,860 parameters, which is smaller than existing spatiotemporal autoencoder-based methods.

Keywords:

Feature extraction

Modulation

Spatiotemporal phenomena

Computational modeling

Convolution

Wireless communication

Decoding

Automatic modulation classification

spatiotemporal feature representation

autoencoder network

attentive ghost

sharing decoder

deep learning

STARNet: An Efficient Spatiotemporal Feature Sharing Reconstructing Network for Automatic Modulation Classification

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