5 papers
SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems
Giancarlo Maldonado Cárdenas, Carlos A. Astudillo, Nelson L. S. da Fonseca
Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LLM) orchestrator. The challenge…
Wireless Foundation Models: State-of-the-Art and Open Challenges
Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh +3
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapting them to downstream tasks. H…
LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks
Walter P. Casas, Nelson L. S. da Fonseca, and Carlos A. Astudillo
Routing in Low Earth Orbit (LEO) satellite networks is challenging due to highly dynamic topologies and spatio-temporal network conditions. Reinforcement Learning (RL) has emerged…
Data-driven Intra-Autonomous Systems Graph Generator
Caio Vinicius Dadauto, Nelson Luis Saldanha da Fonseca, Ricardo da Silva Torres
Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to captur…
Random Access Based on Maximum Average Distance Code for Massive MTC in Cellular IoT Networks
Carlos A. Astudillo, Ekram Hossain, Nelson L. S. da Fonseca
Code-expanded Random Access (CeRA) is a promising technique for supporting mMTC in cellular IoT networks. However, its potentiality is limited by code ambiguity, which results from…