SOURCE-LINKED INTELLIGENCE

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

CORDIS · observation · Publication date unknown

y 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. Wi-Fi, MIMO, multiplexing, channel sounding, unlicenced spectrum, spectrum efficiency, energy

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recordType

award

status

SIGNED

region

EU

value

396991.08

unit

EUR