Advanced Data and IoT Technologies

Neueste Paper

• A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments

  Muhammad Kabeer, Rosdiadee Nordin, Nadiva Nuriftitah, Sian Lun Lau · 21. Juli 2026

  Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration. Conventional monolithic machine learning models struggle to generalize across diverse operators, mobility modes, and traffic types, leaving a critical stochasticity gap between signal conditions and a…

• MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications

  Anshul Kumar, Gagan Raj Gupta, Manish Rai, Apu Chakraborty, Ashutosh Modi, Abdelaali Chaoub, Soumajit Pramanik, Moyank Giri, Yashwanth Holla, Sunny Kumar, M. V. Kiran Sooraj · 20. April 2026

  Large Language Models (LLMs) have emerged as powerful tools for automating complex reasoning and decision-making tasks. In telecommunications, they hold the potential to transform network optimization, automate troubleshooting, enhance customer support, and ensure regulatory compliance. However, the…

• MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction

  Steven E. Wilson, Sina Khanmohammadi · 19. März 2026

  Graph Neural Networks (GNNs) have been widely used for various learning tasks, ranging from node classification to link prediction. They have demonstrated excellent performance in multiple domains involving graph-structured data. However, an important but less explored learning task is link weight p…

• TelcoAgent-Bench: A Multilingual Benchmark for Telecom AI Agents

  Lina Bariah, Brahim Mefgouda, Farbod Tavakkoli, Enrique Molero, Louis Powell, Merouane Debbah · 17. März 2026

  The integration of large language model (LLM) agents into telecom networks introduces new challenges, related to intent recognition, tool execution, and resolution generation, while taking into consideration different operational constraints. In this paper, we introduce TelcoAgent-Bench and TelcoAge…

• Integration of TinyML and LargeML: A Survey of 6G and Beyond

  Thai-Hoc Vu, Ngo Hoang Tu, Thien Huynh-The, Kyungchun Lee, Sunghwan Kim, Miroslav Voznak, Quoc-Viet Pham · 16. März 2026

  The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such…

• AI/ML in 3GPP 5G Advanced -- Services and Architecture

  Pradnya Taksande, Shwetha Kiran, Pranav Jha, Prasanna Chaporkar · 8. Dezember 2025

  The 3rd Generation Partnership Project (3GPP), the standards body for mobile networks, is in the final phase of Release 19 standardization and is beginning Release 20. Artificial Intelligence/ Machine Learning (AI/ML) has brought about a paradigm shift in technology and it is being adopted across in…

• AI/ML in 3GPP 5G Advanced - Services and Architecture

  Pradnya Taksande, Shwetha Kiran, Pranav Jha, Prasanna Chaporkar · 4. Dezember 2025

  The 3rd Generation Partnership Project (3GPP), the standards body for mobile networks, is in the final phase of Release 19 standardization and is beginning Release 20. Artificial Intelligence/ Machine Learning (AI/ML) has brought about a paradigm shift in technology and it is being adopted across in…