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<p>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.</p>

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Energy-optimized 6G communication framework with intelligent resource allocation for massive IoT networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

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  • Mian Muhammad Kamal,
  • Syed Zain Ul Abideen,
  • Muhammad Sheraz,
  • Habib Khan,
  • Jamal N. A. Hassan,
  • Hamedalneel B. A. Hamid,
  • Luo Yinsheng,
  • Tianjun Ma,
  • Husam S. Samkari,
  • Mohammed F. Allehyani,
  • Muneera Altayeb,
  • Teong Chee Chuah

摘要

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.