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A Comprehensive Design Framework for UE-side and BS-Side RIS Deployments

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arxiv 2404.16607 v3 pith:5S6SQ5PW submitted 2024-04-25 eess.SP

classification eess.SP
keywords designbs-sideparameterspracticalue-sidecomprehensivecriticaldeployments
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Integrating reconfigurable intelligent surfaces (RISs) in emerging communication systems is a fast-growing research field that has recently earned much attention. While implementing RISs near the base station (BS), i.e., BS-side RIS, or user equipment (UE), i.e., UE-side RIS, exhibits optimum performance, understanding the differences between these two deployments in terms of the system design perspective needs to be clarified. Critical design parameters, such as RIS size, phase shift adjustment, control link, and element type (passive/active), require greater clarity across these scenarios. Overlooking the intricacies of such critical design parameters in light of 6G demands endangers practical implementation, widening the gap between theoretical insights and practical applications. In this regard, our study investigates the impact of each RIS deployment strategy on the anticipated 6G requirements and offers tailored RIS design recommendations to fulfill these forward-looking requirements. Through this, we clarify the practical distinctions and propose a comprehensive framework for differentiating between BS-side and UE-side RIS scenarios in terms of their design parameters. Highlighting the unique needs of each and the potential challenges ahead, we aim to fuse the theoretical underpinnings of RIS with tangible implementation considerations, propelling progress in both the academic sphere and the industry.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Localization with Base Station-Integrated Beyond Diagonal RIS

    eess.SP 2024-11 conditional novelty 5.0 of 10

    BD-RIS-based passive beamforming at the base station achieves near-active-array localization accuracy in near-field and far-field, much better than diagonal RIS, according to CRLB simulations.

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