671 citations · 966 across the 48 of their papers we have counts for
52 papers
PhysAI-Bench: A Benchmark for LLM-Based Agentic Decision-Making in Autonomous UAV-Centric Physical AI
Mohamed Amine Ferrag, Merouane Debbah, Abderrahmane Lakas +2
Recent advances in Physical AI have accelerated the use of foundation models in autonomous systems such as unmanned aerial vehicles (UAVs), which must perceive, reason, plan, and a…
LIDSA: Cognitive Arbitration for Signal-Free Autonomous Intersection Management
Abderrahmane Lakas, Mohamed Amine Ferrag, Merouane Debbah
Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoning and multi-agent coordination.…
VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
Mohamed Lakas, Mohamed Amine Ferrag
Federated learning (FL) systems typically employ stateless client selection, treating each communication round independently and ignoring accumulated evidence of client contributio…
6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Sixth-generation (6G) networks are increasingly envisioned as AI-native infrastructures integrating communication, sensing, and computing into a unified fabric. However, existing a…
FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi +3
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require…
How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks
Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Emerging 6G visions, reflected in ongoing standardization efforts within 3GPP, IETF, ETSI, ITU-T, and the O-RAN Alliance, increasingly characterize networks as AI-native systems in…