Objective
CONTRACT sets out to investigate a novel, theoretically principled, framework for the reliable deployment of artificial intelligence (AI)-based black-box models in wireless systems. Given the highly non-deterministic patterns of traffic and connectivity conditions, AI is currently viewed as an essential technology to ensure the support of communication services with widely heterogeneous performance requirements, ranging from cloud gaming to Industry 4.0. However, AI ``apps'' are, by and large, black boxes, whose introduction within the larger wireless network raises critical concerns about reliability. These concerns are not addressed by the vast literature on AI for wireless systems, which adopts a standard ``train-and-deploy'' best-effort approach. The main goal of CONTRACT is to endow for the first time AI-based wireless deployments with formal reliability guarantees. To this end, CONTRACT will introduce novel methodologies for: (i) the offline calibration of pre-trained AI apps via hyperparameter optimization prior to deployment; and (ii) the online run-time monitoring of key performance indicators and conflicts among simultaneously deployed apps. In CONTRACT, hyperparameter optimization and online monitoring will leverage contextual information describing traffic and connectivity conditions, as well as synthetic data from a digital twin, i.e. a simulator, of the wireless network. The research will build on conformal prediction, a statistical framework for assumption-free calibration, and on prediction-powered inference as a mechanism for the reliable integration of synthetic data. This project will lead to a reconsideration of the role of AI in the engineering of wireless networks from a potential reliability concern to a dependable part of the system.
Fields of science (EuroSciVoc)
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-ADG
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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.
E1W 1LP London
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.