Deliverables
D2.1 – Definition of use-cases, scenarios, and requirements, by Pavel Mach, Zdenek Becvar (CTU), Pietro Michiardi, Alberto Foresti (EURECOM), Mehdi Bennis, Charbel Bou Chaaya, Tamara Alshammari (UOULU), Jiri Marsik (BOSCH), 2024.
D2.2 – Architecture of the system, by Pavel Mach, Zdenek Becvar (CTU), Mehdi Bennis (UOULU), Jiri Marsik (BOSCH), Pietro Michiardi (EURECOM), 2024.
D2.3 – Business aspects, by Jiri Marsik (BOSCH), Zdenek Becvar, Pavel Mach (CTU), Pietro Michiardi (EURECOM), Mehdi Bennis (UOULU), 2024.
Publications
[1] M. Bounoua, G. Franzese, P. Michiardi, “SΩI: Score-based O-INFORMATION Estimation,” 2024 International Conference on Machine Learning (ICML 2024), 2024. https://arxiv.org/abs/2402.05667
[2] C. Bou Chaaya and M. Bennis, “RIS Phase Optimization via Generative Flow Networks,” IEEE Wireless Communications Letters, vol. 13, no. 7, pp. 1988-1992, July 2024.
[3] J. Choi, J. Park, S. -W. Ko, J. Choi, M. Bennis and S. -L. Kim, “Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication,” IEEE Transactions on Vehicular Technology, vol. 73, no. 10, pp. 15815-15819, Oct. 2024
[4] M. Kishani, Z. Becvar, “Sharing Semantic Information among Vehicles to Reduce Computation and Communication Energy Consumption,” IEEE Vehicular Technology Conference (VTC2025-Spring), 2025.
[5] A. Foresti, G. Franzese, P. Michiardi, “INFO-SEDD: Continuous Time Markov Chains as Scalable Information Metrics Estimators,” International Conference on Learning Representations (ICLR 2025) workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy, 2025.
[6] G. Franzese, M. Martini, G. Corallo, P. Papotti, P. Michiardi, “Latent Abstractions in Generative Diffusion Models,” Entropy, 2025.
[7] C. Bou Chaaya, A. M. Girgis and M. Bennis, “Learning Latent Wireless Dynamics From Channel State Information,” IEEE Wireless Communications Letters, vol. 14, no. 2, pp. 489-493, Feb. 2025.
[8] P. Mach, Z. Becvar, M. Kishani, M. Bennis, “Architecture for AI-enabled Multimodal Semantic Communication and Computing,” IEEE Vehicular Technology Conference (VTC2025-Spring) workshop on Multi-modal and Generative Semantic Communications Towards Cloud-Edge-End Intelligence, 2025.
[9] T. Nishio, C. Chen and M. Bennis, “A Semantic Inpainting Framework for Distributed Cross-Modal Integrated Sensing and Communication,” 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Barcelona, Spain, 2025.
[10] C. Bou Chaaya, A. M. Girgis and M. Bennis, “From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2025 (PIMRC), 2025.
[11] C. Bou Chaaya and M. Bennis, “GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2025 (PIMRC), 2025.
[12] C. Ben Issaid, P. Vepakomma and M. Bennis, “Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach,” Transactions on Machine Learning Research, 2025.
[13] L. Qiao, M. B. Mashhadi, Z. Gao, C. H. Foh, P. Xiao and M. Bennis, “Latency-Aware Generative Semantic Communications With Pre-Trained Diffusion Models,” IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2652-2656, Oct. 2024.
Talks
[1] M. Bennis, “Semantics-native Communication and Protocol Learning in the 6G Era”, Keynote at IEEE WCNC 2024, Dubai.
[2] P. Michiardi, “MI is all you need: Understanding complex multivariate systems through the lenses of GenAI,” Invited talk at One6G Summit 2024, Valencia.
[3] M. Bennis, “Semantics-native Communication and Protocol Learning in the 6G Era”, Keynote at 6Gnet 2024, Paris.
[4] J. Marsik, Z. Becvar, “6G Networks in the Factories of the Future,” Trends in Automotive Logistics (TAL) 2025, Pilsen, Czech Republic.
[5] Z. Becvar, “6G mobile networks,” Internet v Telci 2025, Telc, Czech Republic.