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The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G., , , , , , , , , and 13 other author(s). IEEE Access, (2023)New Access and Flexible Topologies in 6G: Architectural Implications., , , , , , , , , and 4 other author(s). EuCNC/6G Summit, page 682-687. IEEE, (2024)AI-driven, Context-Aware Profiling for 5G and Beyond Networks., , , and . IEEE Trans. Netw. Serv. Manag., 19 (2): 1036-1048 (2022)5Growth Data-Driven AI-Based Scaling., , , , , , , , , and 4 other author(s). EuCNC/6G Summit, page 383-388. IEEE, (2021)Framework for Trustworthy AI/ML in B5G/6G., and . 6GNet, page 1-6. IEEE, (2022)A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks., , , and . Comput. Networks, (2021)Towards 3D Flexible 6G Networks: Status, Challenges and Technical Enablers., , , and . FNWF, page 664-669. IEEE, (2022)A context extraction and profiling engine for 5G network resource mapping., , , , , , , and . Comput. Commun., (2017)An efficient RAT selection mechanism for 5G cellular networks., , , and . IWCMC, page 942-947. IEEE, (2014)5 Growth: Secure and Reliable Network Slicing for Verticals., , , , , , , , , and 11 other author(s). EuCNC/6G Summit, page 347-352. IEEE, (2021)