C. Chiasserini

Goal-Oriented Communication With Adaptive Semantic Representation Selection for Satellite Image

Networks for

Latency-constrained Fidelity Optimization for NISQ Execution via Quantum Circuit Knitting

In-network Processing and Resource Optimization over Mobile-Health

Channel surfaces in Lie sphere geometry

Semantic and Channel-Aware Image Transfer in Satellite Networks

—Non-Terrestrial Networks (NTNs) are expected to support future large-scale remote sensing, but transmitting high-resolution satellite imagery is challenged by limited feeder links, time-varying channels, and constrained onboard computation. We propose AITACS (Adaptive Image Transmission with Asymmetric Computation ove...

: Context-Aware Collaborative Perception in Autonomous Driving Dataset (ConVeX)

ConVeX is introduced, an extensive multi-agent synthetic dataset for collaborative perception (CP) that reproduces different realistic driving scenarios (urban, rural, highway), road layouts, and weather and lighting conditions and ground-truth annotations for object detection.

Distributional Reinforcement Learning for task offloading, resource allocation and early exit selection at the edge

Simulation results confirm that integrating EE with edge computing significantly improves the trade-off between inference accuracy and latency, achieving up to 212% improvement in the average task completion ratio compared to edge computing systems without EE, under the considered simulation settings.

Efficient Federated Learning With Adaptive Model Pruning and Quantization Over Wireless Networks

Federated learning (FL) enables multiple devices to collaboratively train a global model without sharing local data. However, due to limited local computing capability and communication bandwidth, FL suffers from high learning latency, especially when the model size is large. To address these issues, we propose APQ-FL,...

Multi-stack Simulation

An open-source simulation framework for the ns-3 simulator, featuring state-of-the-art vehicular communication models, in which the mobility is managed by the SUMO (Simulation of Urban MObility) simulator, designed to make it easier to con � gure the communication stacks, and to enable a fast and easy deployment of veh...

Sum-Rate Maximization in IRS–NOMA O-RAN

A cross-layer secure sum-rate maximization framework that jointly addresses physical-layer secrecy and network-layer packet loss in IRS–NOMA O-RAN environments and develops SecureO-RAN-SAC, a deep reinforcement learning algorithm based on Soft Actor-Critic v2, which learns near-optimal policies in real time.

Reliability and Traffic Aware Resource Allocation for UAV-Assisted Vehicular O-RAN

The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dyna...