3 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
Improving 5G AI-RAN MCS Selection by Predicting Retransmissions
Tamerlan Aghayev, Maxime Elkael, Michele Polese +5
Link Adaptation (LA) in 5G NR is inherently reactive, relying on channel measurements and HARQ feedback that may become quickly obsolete when the channel changes quickly. This data…
MAC-Gyver: Open, Programmable, Scheduling for AI-RAN 6G Systems
Maxime Elkael, Reshma Prasad, Tamerlan Aghayev +3
Cellular networks are integrating Artificial Intelli- gence (AI) into radio access network control. The MAC scheduler is a promising target because it allocates a limited resource,…
AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling
Maxime Elkael, Michele Polese, Yunseong Lee +2
Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial a…
GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing
Tamerlan Aghayev, Maxime Elkael, Michele Polese +11
Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features fr…
TENORAN: Automating Fine-grained Energy Efficiency Profiling in Open RAN Systems
Ravis Shirkhani, Stefano Maxenti, Leonardo Bonati +7
The transition to disaggregated and interoperable Open Radio Access Network (RAN) architectures and the introduction of RAN Intelligent Controllers (RICs) in O-RAN creates new reso…