Project description
A new lead for wireless AI
Wireless communication systems are facing increasing challenges as they need to handle more data in dynamic environments. Traditional methods for designing receivers are struggling to keep up with the fast changes in wireless channels. Adding to this, devices have limited power and computing resources, making it difficult to process large amounts of data. Current AI solutions, relying on massive pretrained networks, are not well-suited for these conditions. In this context, the ERC-funded FLAIR project aims to tackle these issues by creating a new, flexible form of AI designed specifically for wireless communications. It focuses on lightweight receiver designs, continuous learning and efficient use of data, offering a more adaptable and resource friendly approach.
Objective
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses unique challenges that fundamentally differ from those encountered in traditional deep learning domains. The main challenges arise from the dynamic nature of wireless communications, which causes continual changes to the data distribution, combined with the limited power and computational resources of wireless devices. These challenges impair conventional AI based on offline trained massive DNNs. Our ambitious goal is to introduce a new form of flexible lightweight AI that is particularly tailored for wireless communications. Our approach is based on a holistically revisiting the three fundamental pillars of AI – the architecture, dictating the family of learned mappings; the training algorithm that tunes the architecture; and the data based on which learning is carried out. Accordingly, we focus on three objectives – 1) design trainable receiver architectures that are lightweight and support adaptation to rapid channel variations; 2) establish a new learning paradigm that deviates from conventional training, and is based on viewing continual learning as a dynamic system; and 3) propose techniques to accumulate online data sets that are sufficiently informative for learning purposes while being small enough not to induce notable complexity in training. This is a fundamental depart from conventional deep learning, based on highly-parameterized DNNs trained with massive data sets using lengthy learning procedures. Our preliminary data show that this paradigm shift achieves substantial performance, robustness, and complexity gains over conventional deep receivers. The project will transform how communications systems are studied, and profoundly impact a multitude of applications that rely on wireless communications.
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
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-STG
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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.
84105 Beer Sheva
Israel
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