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DRL-AdaPart optimizes STAR-RIS resource allocation for fair and efficient data rates

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DRL-AdaPart, a novel method utilizing deep reinforcement learning to optimize resource allocation for Simultaneously Transmitting and Reflecting Intelligent Surfaces (STAR-RIS). This approach aims to ensure fair and efficient data rates for users by intelligently assigning STAR-RIS elements and optimizing phase shifts. The DRL algorithm can deactivate unused STAR-RIS elements, leading to significant energy savings without compromising performance, as demonstrated by simulations showing up to 27% deactivation in static scenarios. AI

IMPACT This research could lead to more efficient wireless communication systems by optimizing resource allocation in STAR-RIS environments.

RANK_REASON The item is a research paper submitted to arXiv detailing a new method for resource utilization in STAR-RIS. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DRL-AdaPart optimizes STAR-RIS resource allocation for fair and efficient data rates

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The item is a research paper submitted to arXiv detailing a new method for resource utilization in STAR-RIS. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashok S. Kumar, Nancy Nayak, Sheetal Kalyani, Himal A. Suraweera, Lajos Hanzo ·

    DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization

    arXiv:2407.06868v3 Announce Type: replace-cross Abstract: In this work, we propose a method for efficient resource utilization of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) elements to ensure fair and high data rates. We introduce a s…

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