SNR-Enhanced Automatic Modulation Classification in Artificial Intelligence of Things for Consumer Electronics

Abstract

Automatic modulation classification (AMC) is paramount within the Artificial Intelligence of Things (AIoT) realm for consumer electronics, offering advantages such as efficient spectrum utilization, heightened communication reliability and security, and an enhanced user experience. Addressing the challenges posed by variable signal-to-noise ratio (SNR) conditions, this paper introduces SEMIN (SNR-Enhanced Modulation Insight Network), a novel deep learning architecture aimed at significantly improving classification accuracy, particularly in high SNR scenarios. By integrating SNR-aware training and a unique combination of cross-entropy and center loss functions, SEMIN adeptly balances spatial and temporal feature extraction through convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). Comprehensive evaluations showcase the superior performance of the proposed SEMIN model, achieving an accuracy rate above 93% in high SNR conditions and surpassing existing methods. This outcome not only underscores the effectiveness of the proposed SEMIN model in modulation classification but also establishes a new benchmark for future research and application in relevant fields.