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

Project description

Reliable sensing-aided positioning technology for networks

Sensing-aided positioning technology is a promising innovation poised to support future network technologies by delivering greater accuracy and reduced latency compared to current data communication methods. However, the ultra-low power consumption requirements of many IoT applications pose significant constraints, limiting the adoption of sensing-aided positioning systems that are not yet optimised for these conditions. Supported by the Marie Skłodowska-Curie Actions programme, the SPRING-6G project will tackle key challenges that currently hinder the widespread deployment of reliable and efficient positioning services in 5G and beyond. The project will develop novel solutions to enhance resolution, accuracy, and sensing capabilities, while also reducing processing latency and hardware complexity. Its goal is to lay the groundwork for energy-efficient positioning systems.

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.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

MITTUNIVERSITETET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 252 180,00
Address
HOLMGATAN 10
85170 Sundsvall
Sweden

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Region
Norra Sverige Mellersta Norrland Västernorrlands län
Activity type
Higher or Secondary Education Establishments
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Total cost

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