Green Machine Learning for 5G and Beyond Resource Optimisation

Project Information

GreenML5G

Grant agreement ID: 891221

DOI 10.3030/891221

Project terminated on 15 December 2021

EC signature date 21 April 2020

Start date 1 April 2021

End date 31 December 2023

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost € 224 933,76

EU contribution € 224 933,76

Coordinated by

CRANFIELD UNIVERSITY
United Kingdom

Project description

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.

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 technology › electrical engineering, electronic engineering, information engineering › information engineering › telecommunications › telecommunications networks › mobile network › 5G
• natural sciences › computer and information sciences › artificial intelligence › machine learning › reinforcement learning
• natural sciences › computer and information sciences › artificial intelligence › machine learning › deep learning

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

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
• Region: East of England › Bedfordshire and Hertfordshire › Central Bedfordshire
• Activity type: Higher or Secondary Education Establishments
• Total cost: € 224 933,76