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20072026
most citedSmart Radio Environments Empowered by AI Reconfigurable Meta-Surfaces: An Idea Whose Time Has Come

50 citations · 240 across the 54 of their papers we have counts for

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Showing 2025Show all

11 papers · 1 filter

cs.IT2025

Context Video Semantic Transmission with Variable Length and Rate Coding over MIMO Channels

Bingyan Xie, Yongpeng Wu, Wenjun Zhang +2

The evolution of semantic communications has profoundly impacted wireless video transmission, whose applications dominate driver of modern bandwidth consumption. However, most exis…

cs.AI2025

UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios

Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah

Autonomous aerial systems increasingly rely on large language models (LLMs) for mission planning, perception, and decision-making, yet the lack of standardized and physically groun…

cs.AI2025

Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

Mohamed Sana, Nicola Piovesan, Antonio De Domenico +4

Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lig…

cs.AI2025

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving

Vincenzo Colle, Mohamed Sana, Nicola Piovesan +3

The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathemati…

cs.IT2025

Semantic-aided Parallel Image Transmission Compatible with Practical System

Mingkai Xu, Yongpeng Wu, Yuxuan Shi +4

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly,…

cs.LG2025

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?

Christophe El Zeinaty, Wassim Hamidouche, Glenn Herrou +2

This paper introduces a novel framework for designing efficient neural network architectures specifically tailored to tiny machine learning (TinyML) platforms. By leveraging large…