Automatic Modulation Classification Using Gated Recurrent Residual Network

Sai Huang

doi:10.1109/JIOT.2020.2991052

2020-08-01

PRE

DOI：10.1109/JIOT.2020.2991052

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摘要

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The development of the Internet-of-Things (IoT) security is comparatively slower than the pace of the IoT innovations. The seamless IoT network operates in an untrusted environment and is exposed to many malicious active attacks. As the process of identifying the modulation format of signals is corrupted by noise and fading, automatic modulation classification (AMC) can be viewed as an effective approach to counter physical-layer threats for IoT as it can detect and identify the pilot jamming, deceptive jamming, and Sybil attacks. Nowadays, data-driven deep learning (DL) techniques, which are capable of extracting discriminative features and perform better robustness to channel and noise conditions, have drawn widespread attention. The deep residual network (ResNet) has a strong representative ability, which can learn latent information repeatedly from the received signals and improve the classification accuracy. Meanwhile, the gated recurrent unit (GRU), which is capable of exploiting temporal information of the received signal can expand the dimension of the signal features for satisfactory classification performance. Considering the advantages of the above networks, this article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet. In GrrNet, a ResNet extractor module is first designed to extract the highly representative features and then temporal information is obtained by the subsequent GRU module which is capable of processing the representative features with the arbitrary length for modulation classification. Moreover, extensive simulations are conducted to verify the classification performance and robustness of the proposed GrrNet and it is shown that GrrNet outperforms other recent DL-based AMC methods. Moreover, the influence of the network parameters, symbol length, and frequency offset on performance is also explored.

Keyword:

Feature extraction

Modulation

Internet of Things

Computational modeling

Computational complexity

Logic gates

Residual neural networks

Automatic modulation classification (AMC)

deep learning (DL)

gated recurrent unit (GRU)

Internet-of-Things (IoT) security