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New MM-RIS architecture enhances wireless communication with hybrid reinforcement learning

Researchers have developed a novel movable-element multi-functional reconfigurable intelligent surface (MM-RIS) architecture designed to enhance wireless communication flexibility and energy sustainability. This architecture integrates element mobility with adaptive signal reflection, transmission, amplification, and energy harvesting capabilities. To optimize spectral efficiency, a hybrid rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) transmission scheme was implemented. The complex optimization problem was addressed using a constrained hybrid deep reinforcement learning approach called CRAFTS, which combines multi-agent proximal policy optimization (MAPPO) and multi-agent deep Q-networks (MADQN) with federated learning and a Lagrangian-based adaptive penalty mechanism. AI

IMPACT This research could lead to more energy-efficient and adaptive next-generation wireless networks by improving spectral efficiency and energy harvesting.

RANK_REASON This is a research paper detailing a novel technical approach to wireless communication. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New MM-RIS architecture enhances wireless communication with hybrid reinforcement learning

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This is a research paper detailing a novel technical approach to wireless communication. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Li-Hsiang Shen, Chi-Te Kuo ·

    Movable-Element Multi-Functional RIS-Assisted Hybrid RSMA-NOMA: A Constrained Hybrid Reinforcement Learning Approach

    arXiv:2610.34701v1 Announce Type: new Abstract: In this work, we propose a movable-element (ME) multi-functional reconfigurable intelligent surface (MF-RIS) architecture, termed MM-RIS, which integrates element mobility with adaptive signal reflection, transmission, amplification…