Objective
Sensing-aided positioning is poised to be a key enabler for future sixth-generation (6G) systems, yet innovative approaches are required to realize its full potential. Future networks will integrate sensing-aided positioning to elevate accuracy and reduce latency, extending capabilities beyond traditional data communication. However, many IoT applications demand ultra-low power consumption, a requirement not fully supported by current 5G networks, posing challenges to the development of accurate sensing-aided positioning systems. This project addresses critical challenges preventing the large-scale deployment of reliable positioning services in 5G and beyond, such as computation latency due to path loss, mmWave channel constraints, range accuracy, angular estimation, and computational complexity. To tackle these issues, we propose SPRING-6G (Sensing-Aided Positioning with Reconfigurable Intelligent Surfaces for Next-Generation 6G), which will explore the design and implementation of sensing and positioning algorithms tailored for Reduced Capability (RedCap) systems, leveraging Reconfigurable Intelligent 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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
You need to log in or register to use this function
Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
85170 Sundsvall
Sweden