12 citations · 28 across the 15 of their papers we have counts for
4 papers · 1 filter
Joint Explainability and Sensitivity-Aware Federated Deep Learning for Transparent 6G RAN Slicing
Swastika Roy, Farhad Rezazadeh, Hatim Chergui +1
In recent years, wireless networks are evolving complex, which upsurges the use of zero-touch artificial intelligence (AI)-driven network automation within the telecommunication in…
SliceOps: Explainable MLOps for Streamlined Automation-Native 6G Networks
Farhad Rezazadeh, Hatim Chergui, Luis Alonso +1
Sixth-generation (6G) network slicing is the backbone of future communications systems. It inaugurates the era of extreme ultra-reliable and low-latency communication (xURLLC) and…
A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti +4
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particul…
Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing
Farhad Rezazadeh, Hatim Chergui, Josep Mangues-Bafalluy
The complexity of emerging sixth-generation (6G) wireless networks has sparked an upsurge in adopting artificial intelligence (AI) to underpin the challenges in network management…