4 citations · 8 across the 5 of their papers we have counts for
5 papers
Deep Reinforcement Learning-Based RAN Slicing with Efficient Inter-Slice Isolation in Tactical Wireless Networks
Abderrahime Filali, Diala Naboulsi, Georges Kaddoum
The next generation of tactical networks (TNs) is poised to further leverage the key enablers of 5G and beyond 5G (B5G) technology, such as radio access network (RAN) slicing and t…
Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks
Amine Abouaomar, Afaf Taik, Abderrahime Filali +1
Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals…
Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning
Abderrahime Filali, Boubakr Nour, Soumaya Cherkaoui +1
The evolution of the future beyond-5G/6G networks towards a service-aware network is based on network slicing technology. With network slicing, communication service providers seek…
Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services
Abderrahime Filali, Zoubeir Mlika, Soumaya Cherkaoui +1
Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communic…
A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks
Amine Abouaomar, Zoubeir Mlika, Abderrahime Filali +2
Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services)…