285 citations · 1.2k across the 69 of their papers we have counts for
9 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,…
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…
ARCHES: Adaptive Real-Time Switching of AI Models for the RAN
Neagin Neasamoni Santhi, Davide Villa, Michele Polese +4
Artificial Intelligence (AI) has become a powerful tool for model-free Radio Access Network (RAN) signal processing and optimization. However, designing a single model that general…
Predicting Conflict Impact on Performance in O-RAN
Pietro Brach del Prever, Niloofar Mohamadi, Salvatore D'Oro +7
The O-RAN Alliance promotes the integration of intelligent autonomous agents to control the Radio Access Network (RAN). This improves flexibility, performance, and observability in…
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…
SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control
MohammadErfan Jabbari, Abhishek Duttagupta, Claudio Fiandrino +5
Deep reinforcement learning (DRL) promises adaptive control for future mobile networks but conventional agents remain reactive: they act on past and current measurements and cannot…
Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control
Amirmohammad Farzaneh, Salvatore D'Oro, Osvaldo Simeone
Large language model (LLM)-powered agents can translate high-level user intents into plans and actions in an environment. Yet after observing an outcome, users may wonder: What if…
MANATEE: A DevOps Platform for xApp Lifecycle Management and Testing in Open RAN
Sofia Montebugnoli, Leonardo Bonati, Andrea Sabbioni +5
The shift to disaggregated 5G architectures introduces unprecedented flexibility but also significant complexity in Beyond 5G Radio Access Networks (RANs). Open RAN enables program…