HEIDIS: HiErarchIcal DIsaggregated Scheduling for beyond-5G networks

HEIDIS: HiErarchIcal DIsaggregated Scheduling for beyond-5G networks

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Démonstration technique à 6GNET 2025

SMILE et CNAM ont effectué une démonstration technique sur l’un des résultats du projet HEIDIS, sur l’intégration de Smart-NIC programmables à l’architecture, au congrès international 6GNET 2025 qui a eu lieu au Cnam du 17 au 19 décembre 2025.

Sahar HOTEIT December 19, 2025December 22, 2025 Uncategorized Read more

Session spéciale à la journée Virtualisation du GdR RSD

Le projet HEIDIS a clôturé ses activités avec une session spéciale à la Journée Virtualisation du GdR RSD (Réseaux et systèmes Distribués) le 17 décembre au Cnam Paris. Trois présentations de la part du L2S et du CNAM ont pris

Sahar HOTEIT December 16, 2025December 22, 2025 Uncategorized Read more

3rd Plenary meeting

The HEIDIS consortium met at Cnam for the third plenary meeting to advance with ongoing collaborative work on OpenRAN scheduling, Smart-NIC design and 5G+ resource allocation.

web_developer June 1, 2024June 1, 2024 Uncategorized Read more

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News

  • Démonstration technique à 6GNET 2025 December 19, 2025
  • Session spéciale à la journée Virtualisation du GdR RSD December 16, 2025
  • 3rd Plenary meeting June 1, 2024

Latest publications

  • Towards Resource-Efficient Next-Generation Mobile Networks: Design and Optimization Strategies
  • Energy-Efficient Placement and Association in Disaggregated O-RAN
  • Multi-Resource Orchestration and Energy-Aware VNF Placement for Open RAN
  • Comparative E2E Performance Analysis of O-RAN Designs in a 5G Standalone Testbed
  • Optimizing URLLC Resource Allocation in Open-RAN: A Transformer-based Approach to Flexible Numerology
  • On flexible association and placement in disaggregated RAN designs
  • Policy-Gradient-based Reinforcement Learning for Maximizing Operator's Profit in Open-RAN
  • Line-rate Botnet Detection with SmartNIC-Embedded Feature Extraction
  • Experiences with disaggregated RAN integrations
  • Using Early-Exit Deep Neural Networks to Accelerate Spectrum Classification in O-RAN
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