9 papers
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…
StormShield: Fingerprint-Based Detection and Mitigation of RRC Signaling Storms in O-RAN 5G RANs
Noemi Giustini, Andrea Lacava, Leonardo Bonati +4
5G networks provide low-latency, high throughput, and massive connectivity, yet the control plane remains exposed to several security threats. Among the most common and impactful t…
BLINC: Context-Specific Causal Learning for Automated RAN Configuration
Reshma Prasad, Michele Polese, Tommaso Melodia
Radio Access Network (RAN) configuration has traditionally required significant manual effort due to indirect causal dependencies between observable Key Performance Indicators (KPI…
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…