14 citations · 21 across the 20 of their papers we have counts for
20 papers
CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations
Xingyu Yan, Tingting Dai, Antonio De Domenico +16
While autonomous agents show promise for network operations, existing telecom troubleshooting evaluations fail to capture the partial observability and heterogeneity of real-world…
HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime
Mohamed Sana, Nicola Piovesan, Antonio De Domenico +2
We investigate a narrow but common failure mode of GRPO-style reinforcement learning in the context of sparse verifiable rewards: early updates contain more responses with negative…
TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation
Anas Ezzakri, Nicola Piovesan, Mohamed Sana +3
Large Language Models (LLMs) are increasingly applied to telecom engineering tasks, yet perform poorly on 3GPP specifications. These standards encode much of their technical inform…
KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
Dmitry Akulov, Mohamed Sana, Antonio De Domenico +3
Large language models (LLMs) rely on key-value (KV) caches for efficient autoregressive decoding; however, cache size grows linearly with context length and model depth, becoming a…
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…
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…
A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks
Henri Alam, Antonio de Domenico, Tareq Si Salem +1
Integrated terrestrial and non-terrestrial network (TN-NTN) architectures offer a promising solution for expanding coverage and improving capacity for the network. While non-terres…
Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing
Andrei-Laurentiu Bornea, Fadhel Ayed, Antonio De Domenico +3
Artificial intelligence will be one of the key pillars of the next generation of mobile networks (6G), as it is expected to provide novel added-value services and improve network p…
Goal-Oriented Time-Series Forecasting: Foundation Framework Design
Luca-Andrei Fechete, Mohamed Sana, Fadhel Ayed +4
Conventional time-series forecasting methods typically aim to minimize overall prediction error, without accounting for the varying importance of different forecast ranges in downs…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
Hermes: A Large Language Model Framework on the Journey to Autonomous Networks
Fadhel Ayed, Ali Maatouk, Nicola Piovesan +3
The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reac…
Optimizing Integrated Terrestrial and Non-Terrestrial Networks Performance with Traffic-Aware Resource Management
Henri Alam, Antonio de Domenico, David López-Pérez +1
To address an ever-increasing demand for ubiquitous high-speed connectivity, mobile network deployments are becoming increasingly dense. However, this densification has also led to…
Pay Attention to What Matters
Pedro Luiz Silva, Antonio de Domenico, Ali Maatouk +1
Despite the remarkable success of Large Language Models (LLMs), they still exhibit a limited capability to align their outputs to the user instructions. In this work, we introduce…
Waste Factor and Waste Figure: A Unified Theory for Modeling and Analyzing Wasted Power in Radio Access Networks for Improved Sustainability
Theodore S. Rappaport, Mingjun Ying, Nicola Piovesan +2
This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems ap…
Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
Andrei-Laurentiu Bornea, Fadhel Ayed, Antonio De Domenico +2
The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the co…
Telecom Language Models: Must They Be Large?
Nicola Piovesan, Antonio De Domenico, Fadhel Ayed
The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency. However, the deplo…
Linguistic Intelligence in Large Language Models for Telecommunications
Tasnim Ahmed, Nicola Piovesan, Antonio De Domenico +1
Large Language Models (LLMs) have emerged as a significant advancement in the field of Natural Language Processing (NLP), demonstrating remarkable capabilities in language generati…
Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach
David López-Pérez, Antonio De Domenico, Nicola Piovesan +1
The energy consumption of mobile networks poses a critical challenge. Mitigating this concern necessitates the deployment and optimization of network energy-saving solutions, such…
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments
Mert Unsal, Ali Maatouk, Antonio De Domenico +2
As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments. The size of deep learning models makes it difficult to de…
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Ali Maatouk, Fadhel Ayed, Nicola Piovesan +3
We introduce TeleQnA, the first benchmark dataset designed to evaluate the knowledge of Large Language Models (LLMs) in telecommunications. Comprising 10,000 questions and answers,…