Détail du document
Identifiant
oai:arXiv.org:2408.13781
Sujet
Computer Science - Networking and ...Auteur
Rezazadeh, Farhad Gargari, Amir Ashtari Lagén, Sandra Mangues, Josep Niyato, Dusit Liu, LingjiaCatégorie
Computer Science
Année
2024
Date de référencement
02/10/2024
Mots clés
generative ns-3Résumé
The move toward Sixth-Generation (6G) networks relies on open interfaces and protocols for seamless interoperability across devices, vendors, and technologies.
In this context, open 6G development involves multiple disciplines and requires advanced simulation approaches for testing.
In this demo paper, we propose a generative simulation approach based on a multi-agent Large Language Model (LLM) and Network Simulator 3 (ns-3), called Generative Open xG Network Simulation (GenOnet), to effectively generate, debug, execute, and interpret simulated Open Fifth-Generation (5G) environments.
The first version of GenOnet application represents a specialized adaptation of the OpenAI GPT models.
It incorporates supplementary tools, agents, 5G standards, and seamless integration with ns-3 simulation capabilities, supporting both C++ variants and Python implementations.
This release complies with the latest Open Radio Access Network (O-RAN) and 3GPP standards.
;Comment: 3 pages, 4 figures
Rezazadeh, Farhad,Gargari, Amir Ashtari,Lagén, Sandra,Mangues, Josep,Niyato, Dusit,Liu, Lingjia, 2024, GenOnet: Generative Open xG Network Simulation with Multi-Agent LLM and ns-3
Projection-Specific Heterogeneity of the Axon Initial Segment of Pyramidal Neurons in the Prelimbic Cortex
heterogeneity projection-specific
