12 citations · 15 across the 6 of their papers we have counts for
12 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…
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case
Swastika Roy, Hatim Chergui, Christos Verikoukis
In the context of sixth-generation (6G) networks, where diverse network slices coexist, the adoption of AI-driven zero-touch management and orchestration (MANO) becomes crucial. Ho…
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
Swastika Roy, Hatim Chergui, Christos Verikoukis
Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building trust in the AI black boxes via explainable artificial intelligence (XAI), where it is…
Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research
Bouziane Brik, Hatim Chergui, Lanfranco Zanzi +5
The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop c…
On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti +3
Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, mos…