SOURCE-LINKED INTELLIGENCE

Sensing-Aided Positioning with Reconfigurable Intelligent Surfaces for Next-Generation 6G (SPRING-6G)

CORDIS · observation · Publication date unknown

t Surfaces (RIS) for enhanced control. We will develop a high-resolution iterative estimation algorithm utilizing the line-of-sight (LoS) path with RIS for uplink 6G terahertz (THz) networks and also machine learning (ML)-based beam management framework that is computationally efficient and converge reachable, which minimizes large training overhead and optimizes sensing accuracy above 95% and latency from 500 to 100 ms or less and 30% system complexity reduction when compared to state-of-the-art sensing-aided positioning methods. The SPRING-6G project is centered around four key objectives: enhancing sensing capabilities, improving accuracy and resolution, reducing processing latency, and managing hardware complexity, paving the way for scalable, efficient, and highly accurate positioning systems in 6G networks. Sixth generation (6G), reduced capability (RedCap) device, ssensing, positioning, angle of arrival (AoA) estimation, machine learning (ML), and wireless communication

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recordType

award

status

SIGNED

region

EU

value

252180

unit

EUR