HORIZON-JU-SNS-2024

ExpectedOutcome : The key expected outcomes include: Realistic applicability of AI at large scale in 6G networks for natively supporting AI architectures, common data sets and/or federated learning methodologies and assessment models, including re-training of models with the introduction/update of the data sets; AI/ML solutions that will have impactful contribution to standardisation activities; Interpretability solution exploring standard-compliance testing & debugging techniques. Development of curated data sets of realistic 6G scenarios (using new real and/or synthetic data sets) for reference usage in telecommunication research and standardisation, targeting their wide acceptance and future usage for benchmarking by future EU R&I activities. Analysis, aggregation and harmonisation of results from existing projects and creation of an overall framework for benchmarking and calibration, end-to-end testing and evaluation of AI solutions for 6G networks. Metrics and models to assess the pros and cons of AI technologies in telecommunications, including aspects as energy efficiency, explainability, reliability, safety and security, non-discrimination, privacy and performance as well as usability & accessibility for users. Specific focus should be on energy-efficiency and computational complexity that are still open issues for real-time hardware. Recommendations for policy and regulatory guidelines on the development and usage of AI solutions for network optimisations and provision of AI as a service. Development of a trustworthy AI framework which should be addressed in each stage of the AI system building (from data to model development etc.). Focus should be on implementation and connected to current standardization efforts and state-of-the-art Open Source frameworks and tooling. Objective : Please refer to the "Specific Challenges and Objectives" section for Stream B in the Work Programme, available under ‘Topic Conditions and Documents - Additional Documents’. Scope : The focus of this Strand is on several complementary issues and applicants may select several or all the below-mentioned issues. The main goal of this project is to fill the gaps and work on the end-to-end system integration of SNS AI/ML solutions, or national level developed AI/ML solutions and not to focus on dedicated AI/ML problems of specific network domains. The targeted project scope includes: Development of a reference framework for end-to-end AI usage for the telecommunications domain in relation to 6G, including methodologies for centralized, distributed and federated applications, reference use cases, data acquisition and generation, repositories, curated training and evaluation data, as well as the technologies and functionalities needed to use it as a benchmarking platform for future AI/ML solutions for 6G networks. The framework should be expandable so that future R&I actions can follow its directives and easily provide new use cases and data sets. Towards this end, the reference framework shall be hardware-agnostic, so that it can support heterogeneous hardware implementations. Development of appropriate data infrastructure and functionalities that will enable novel AI-based services as well as AI as a Service to vertical industries. Models for AI costs and benefits in telecommunications applications. Typical 6G metrics should be able to be evaluated, including but not limited to data rate, latency, density, energy efficiency, flexibility and performance, and/or security and privacy, but other value metrics can be considered as well. Solutions that will guarantee reliable use of the technology and build trust in 6G and services enabled by 6G. Associated topics include: i) AI environment (training, development, production) evaluation; ii) assessment models of reliable AI costs and performance value; iii) conflict resolution among local and global AI models, iv) Vulnerability assessment of AI models for different telecommunication ap