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20242026
most citedA Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challenges

42 citations · 81 across the 28 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.NI2026

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…

cs.NI2026

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,…

cs.NI2026

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…

cs.NI2026

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…

cs.NI2026

xDevSM: An Open-Source Framework for Portable, AI-Ready xApps Across Heterogeneous O-RAN Deployments

Angelo Feraudo, Stefano Maxenti, Andrea Lacava +4

Openness and programmability in the O-RAN architecture enable closed-loop control of the Radio Access Network (RAN). Artificial Intelligence (AI)-driven xApps, in the near-real-tim…

cs.NI2026

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…

cs.NI2026

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…