Advancing green mobile networks
Journal
Nature Communications
Authors: Antonio De Domenico, Nicola Piovesan, Xiaoyi Zhou, Xiaowen Li, Magnus Olsson, Xin Chen
Mobile communication is a cornerstone technology in our society, facilitating personal relationships, fostering cooperation, and advancing education. Each generation of this technology has brought new services, from voice calls and messaging to internet access, and now Artificial Intelligence (AI). This transformation has steadily augmented the mobile network resource requirements. Driven by the societal and economical needs to increase the mobile network energy efficiency, reduce its energy consumption and the associated environmental footprint, this comment surveys standardized metrics to assess energy-related network performance and overview data-driven state-of-the-art solutions to accelerate the energy transition of mobile networks.
TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation
Preprint
Authors: Anas Ezzakri, Nicola Piovesan, Mohamed Sana, Antonio De Domenico, Fadhel Ayed, Haozhe Zhang
Language Models (LLMs) are increasingly explored in the telecom industry to support engineering tasks, accelerate troubleshooting, and assist in interpreting complex technical documents. However, recent studies show that LLMs perform poorly on telecom standards, particularly 3GPP specifications. We argue that a key reason is that these standards densely include tables to present essential information, yet the LLM knowledge and interpretation ability of such tables remains largely unexamined. To address this gap, we introduce TeleTables, a benchmark designed to evaluate both the implicit knowledge LLMs have about tables in technical specifications and their explicit ability to interpret them. TeleTables is built through a novel multi-stage data generation pipeline that extracts tables from 3GPP standards and uses multimodal and reasoning-oriented LLMs to generate and validate questions. The resulting dataset, which is publicly available, comprises 500 human-verified question-answer pairs, each associated with the corresponding table in multiple formats. Our evaluation shows that, smaller models (under 10B parameters) struggle both to recall 3GPP knowledge and to interpret tables, indicating the limited exposure to telecom standards in their pretraining and the insufficient inductive biases for navigating complex technical material. Larger models, on the other hand, show stronger reasoning on table interpretation. Overall, TeleTables highlights the need for domain-specialized fine-tuning to reliably interpret and reason over telecom standards.
