Project description DEENESFRITPL Making green machine learning algorithms The greening of our communication networks is an emerging trend, but potentially threatened by energy intensive AI algorithms that are needed for complex optimisations. The EU-funded GreenML5G project will investigate how to reduce energy expenditure for deep reinforcement learning modules. The overall output of the project is to create green machine learning algorithms for radio resource management. The technology has widespread impact in other areas of autonomy and machine learning. Show the project objective Hide the project objective Objective Artificial Intelligence (AI) is revolutionising a wide range of industries. Wireless networks with emerging high dimensional challenges are set to benefit from data-driven deep learning optimisation across layers. In particular, we expect that the deep supervised and deep reinforcement learning modules can resolve high-dimensionality inputs, achieve near optimal solutions, and efficiently scale via confederated learning. However, what is not well understood is the energy cost and carbon footprint of AI in future wireless networks. The danger is that intelligent networks are not green networks and that the recent progress made in green communication risk being undermined by the new breed of AI-based wireless communication. Here, in this project, we propose to develop green machine learning algorithms for radio resource management. This will lead to a future of intelligent and sustainable wireless networking. Fields of science engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5Gnatural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learningnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningengineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technology Keywords resource management energy efficiency Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2019 - Individual Fellowships Call for proposal H2020-MSCA-IF-2019 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator CRANFIELD UNIVERSITY Net EU contribution € 224 933,76 Address College road MK43 0AL Cranfield - bedfordshire United Kingdom See on map Region East of England Bedfordshire and Hertfordshire Central Bedfordshire Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00