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A data-driven context-adaptive channel sounding method for large-scale multiple-input multiple-output (MIMO) systems to boost next-generation Wi-Fi network efficiency

Project description

Smarter, faster Wi-Fi for the virtual age

As virtual worlds become part of daily life, our reliance on wireless connectivity is growing. Yet today’s Wi-Fi networks are struggling to keep up with the flood of connected devices and high-bandwidth applications. Supported by the Marie Skłodowska-Curie Actions programme, the MIMOSA project is tackling this problem by rethinking how Wi-Fi networks handle the simultaneous transmission of multiple data streams. Instead of relying on currently-adopted resource-heavy methods to coordinate antennas, MIMOSA is developing smart, deep learning-based techniques to streamline communication. By slashing the overhead required for MIMO (multiple-input, multiple-output) transmission coordination, the project aims to unlock faster, more efficient indoor connectivity. The goal is to establish gigabit-speed Wi-Fi that supports immersive digital experiences.

Objective

The digitalisation of society is leading to the creation of virtual worlds where people can meet, work, and access services remotely. This calls for fast and reliable wireless connectivity to provide Internet users with a fully immersive experience. While Wi-Fi is ubiquitously being used for indoor connectivity, current networks are unable to fulfil the data rate requirement of emerging applications and the massive service request of the huge number of Wi-Fi terminals, which are growing at a rate of more than 4 billion devices per year. The multiple-input multiple-output (MIMO) technology would theoretically make Wi-Fi able to meet such requirements by concurrently transmitting several data streams via multiple antennas. However, the airtime overhead imposed by the periodic exchange of control data for channel sounding – the procedure that enables MIMO transmissions – does not scale well with an increasing number of antennas. To address this daunting challenge, MIMOSA will lay the technical and scientific foundations of a new method for MIMO channel sounding that will drastically reduce the airtime overhead. MIMOSA will research and develop advanced signal processing and deep learning algorithms to customise the sounding to the specific MIMO deployment and propagation environment, while controlling the computational complexity based on the required communication performance. MIMOSA's sounding will enable the efficient implementation of large-scale MIMO, which allows serving multiple devices simultaneously without impacting the network energy budget. This will help reach the EU's Digital Decade target of providing all households with Gigabit connectivity through sustainable networks by 2030. The research will be conducted at Northeastern University (USA) and the University of Padova (Italy). MIMOSA's sounding will be designed and evaluated via extensive data-collection campaigns with custom testbeds and commercial Wi-Fi devices available at the two institutions.

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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-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global 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

UNIVERSITA DEGLI STUDI DI PADOVA
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.

€ 396 991,08
Address
VIA 8 FEBBRAIO 2
35122 PADOVA
Italy

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Region
Nord-Est Veneto Padova
Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (2)

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