LATIDIA · Investigación

Redes de IA genéticas para sistemas aéreos no tripulados heterogéneos en redes inalámbricas de baja altitud

arXiv:2609.19538v1 Tipo de anuncio: nuevo Resumen: Las redes inalámbricas de baja altitud (LAWN) se están convirtiendo en una infraestructura clave para sistemas aéreos no tripulados heterogéneos que admiten servicios concurrentes dentro de un

La noticia

arXiv:2609.19538v1 Announce Type: new Abstract: Low-altitude wireless networks (LAWNs) are emerging as a key infrastructure for heterogeneous unmanned aerial systems that support concurrent services within a shared three-dimensional airspace. Their coexistence creates strong coupling among mobility, connectivity, and shared network resources, while heterogeneous services impose distinct and time-varying requirements. These interactions naturally form a dynamic non-cooperative game in which both operating conditions and coordination objectives evolve over time. Conventional optimization and learning-based controllers typically rely on predefined objectives, limiting their ability to adapt autonomously to changing service requirements and resource priorities. To address this challenge, we propose a hierarchical hybrid large language model (LLM)- multi-agent reinforcement learning (MARL) architecture organized