Xiang, Wei, Zia, Muhammad Umer, Ahmad, Jameel, Cheng, Peng, Yu, Kan, and Huang, Tao (2025) Deep Learning-enabled RIS Massive MIMO Systems for Industrial IoT: A Joint Communication and Computation Approach. IEEE Journal on Selected Areas in Communications. (In Press)

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Abstract

Accurate estimation and detection, along with phase shift optimization, are vital for implementing reconfigurable intelligent surface (RIS)-enabled multi-antenna systems in highly disruptive industrial IoT environments. Motivated by the remarkable capabilities of deep learning (DL) techniques, this paper introduces a pioneering approach to address challenges in channel estimation, channel correlation prediction, and symbol detection for industrial IoT. We develop an optimization framework for large-scale IoT deployments to maximize the signal-to-interference-plus-noise ratio (SINR) while minimizing transmit power. We also propose a transformer-based channel correlation predictor for IoT devices, which enables adaptive pilot retransmissions and reduces training overhead through a co-design approach that integrates communication, computation, and control. Extensive simulations under realistic, time-varying industrial IoT channel conditions demonstrate the superiority of our DL-driven approach, achieving significant improvements in detection accuracy and SINR.

Item ID:	85752
Item Type:	Article (Research - C1)
ISSN:	1558-0008
Copyright Information:	© 2025 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted,
Date Deposited:	09 Jun 2025 22:39
FoR Codes:	40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 70% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 30%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220107 Wireless technologies, networks and services @ 100%
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