TDRA: Transformer-Based Deep Recurrent Architecture for Automatic Modulation Classification Pertinent to Intelligent-Reflecting-Surface-Assisted Internet of Things Networks

Debbarni Sarkar, Yogita, Satyendra Singh Yadav, Linga Reddy Cenkeramaddi, Om Jee Pandey

Abstract: In wireless networks, automatic modulation classification (AMC) is crucial for enabling intelligent signal demodulation, thereby enhancing the system’s adaptability across various applications. Concurrently, the rapid expansion of the Internet of Things (IoT) necessitates scalable network solutions with limited power consumption. Moreover, addressing the Nonline-of-Sight (NLoS) effects in IoT networks, intelligent reflecting surface (IRS) emerges as a promising, cost-effective technology. This article introduces a novel transformer-based deep recurrent architecture (TDRA) for AMC, tailored for IRS-assisted IoT networks, which significantly improves IoT Device (IoTD) performance in NLoS scenarios. In TDRA, the existing recurrent models, long-short-term memory (LSTM), and gated-recurrent-unit (GRU) are suitably revamped with a transformer-based approach and termed as transformer-based LSTM (T-LSTM) and transformer-based GRU (T-GRU). Numerical data sets are generated for IoT applications considering the seven widely used modulation types to train and test the proposed models. Comparative analysis with seven state-of-the-art deep learning models and five machine learning models for AMC demonstrates the superior performance of the proposed models across multiple metrics, including accuracy, R-squared-score, mean-square error, mean-absolute error, precision, recall, and F1-score. Further, the proposed models exhibit notable improvements under various conditions, such as optimized and random IRS phase shifts, with and without IRS-assisted IoT networks, different modulation sequence lengths, and fading channels. Additionally, the time complexity and processing time of the proposed models have been studied to test their suitability for IoTD. The simulation results indicate that the TDRA for AMC in IRS-assisted IoT networks achieves up to 87% higher accuracy compared to without IRS-assisted IoT networks. This significant enhancement underscores the potential of TDRA to revolutionize IoT networks by providing robust, efficient, and scalable solutions for real-world applications.