Citation

Kamal, Mian Muhammad and Ul Abideen, Syed Zain and Sheraz, Muhammad and Khan, Habib Nawaz and Hassan, Jamal N. A. and Hamid, Hamedalneel B. A. and Yinsheng, Luo and Ma, Tianjun and Samkari, Husam S. and Allehyani, Mohammed F. and Altayeb, Muneera and Chuah, Teong Chee (2026) Energy-optimized 6G communication framework with intelligent resource allocation for massive IoT networks. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

This paper proposes an energy-optimized uplink resource allocation framework for 6G massive Internet of Things (IoT) networks assisted by a Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS). Unlike prior works that optimize radio resources and STAR-RIS coefficients separately, we jointly control transmit power, subchannel assignment, and the full set of STAR-RIS amplitude splitting and phase-shift coefficients using a single Soft Actor-Critic (SAC) agent with Gumbel-Softmax relaxation. The resulting policy is trained offline in a centralized manner and executed online with edge cloud coordination. Extensive simulations based on 3GPP Urban Micro channels with up to 200 devices and a 128-element STAR-RIS show that the proposed framework achieves 24.3% higher energy efficiency, 18.7% higher aggregate throughput, 19.1% lower latency, and 21.6% longer network lifetime compared to state-of-the-art successive convex approximation baselines, while maintaining near-optimal fairness. The results demonstrate that tight cross-layer integration of propagation control and radio resource allocation via deep reinforcement learning is a scalable and effective solution for green 6G massive machine-type communications.

Item Type:	Article
Uncontrolled Keywords:	6G communication
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Jun 2026 05:36
Last Modified:	30 Jun 2026 05:36
URII:	http://shdl.mmu.edu.my/id/eprint/16144

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