A Robust Perceiver-Based Automatic Modulation Classification for the Next-Generation of Wireless Communication Networks

Ahmed Alhammadi, Shimaa A. Naser, Sami Muhaidat

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Automatic modulation classification (AMC) is an indispensable part of intelligent receivers in modern wireless communication systems. AMC enables blind identification of modulation without prior knowledge of the signal parameters, which is a challenging task, particularly in practical scenarios with severe multipath fading, frequency-selective and time-varying channels. Although deep learning techniques have been shown to be efficient in AMC tasks, traditional convolutional and recurrent neural networks may not be able to cope with complex-valued input signals and large-scale datasets. Motivated by this, in this paper, we propose a novel perceiver-based AMC architecture that leverages the recently introduced Perceiver, which combines cross-attention and latent transformer modules, to efficiently process and classify complex-valued in-phase and quadrature (IQ) samples of the received signal. The proposed model is trained and evaluated on the DeepSig 2018 RadioML dataset. Simulation results demonstrate a significant improvement in the classification accuracy compared to a ResNet-based AMC model, particularly for higher-order quadrature amplitude modulation (QAM) and under practical signal-to-noise ratio values. These findings indicate the potential of the perceiver architecture for robust and efficient AMC in wireless communication systems.

    Original languageBritish English
    Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2930-2936
    Number of pages7
    ISBN (Electronic)9798350310900
    DOIs
    StatePublished - 2023
    Event2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia
    Duration: 4 Dec 20238 Dec 2023

    Publication series

    NameProceedings - IEEE Global Communications Conference, GLOBECOM
    ISSN (Print)2334-0983
    ISSN (Electronic)2576-6813

    Conference

    Conference2023 IEEE Global Communications Conference, GLOBECOM 2023
    Country/TerritoryMalaysia
    CityKuala Lumpur
    Period4/12/238/12/23

    Keywords

    • Automatic modulation classification
    • Deep learning
    • Perceiver
    • Quadrature amplitude modulation
    • Wireless communication

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