17 papers
A Spatio-Temporal Model for Information Freshness in Massive Random Access
Andrea Munari, Alessandro Buratto, Federico Chiariotti +2
Massive connectivity, a key building block of 5G, is expected to play an important role in the next generation of wireless systems, and its expected requirements are being revoluti…
Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins
Fabio Saggese, Federico Chiariotti, Shashi Raj Pandey +3
Digital twins (DTs) of physical systems enable real-time remote tracking, control, and learning, but require to be updated with environmental sensory data to maintain alignment wit…
Push-Pull Medium Access for Digital Twin Alignment and Low-Latency Anomaly Reporting
Federico Chiariotti, Fabio Saggese, Andrea Munari +2
A digital twin (DT) contains a set of virtual models of real systems and processes that are synchronized with their physical counterparts. In a setup in which contact with the phys…
Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding
Pietro Talli, Federico Mason, Federico Chiariotti +1
Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneously and then takes actions accor…
GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression
Pietro Talli, Qi Liao, Alessandro Lieto +3
Current network data telemetry pipelines consist of massive streams of fine-grained Key Performance Indicators (KPIs) from multiple distributed sources towards central aggregators,…
Medium Access for Push-Pull Data Transmission in 6G Wireless Systems
Shashi Raj Pandey, Fabio Saggese, Junya Shiraishi +2
Medium access in 5G systems was tailored to accommodate diverse traffic classes through network resource slicing. 6G wireless systems are expected to be significantly reliant on Ar…