NextG-GPT: Leveraging GenAI for Advancing Wireless Networks and Communication Research
Journal:
arXiv
Published Date:
May 25, 2025
Abstract
Artificial intelligence (AI) and wireless networking advancements have
created new opportunities to enhance network efficiency and performance. In
this paper, we introduce Next-Generation GPT (NextG-GPT), an innovative
framework that integrates retrieval-augmented generation (RAG) and large
language models (LLMs) within the wireless systems' domain. By leveraging
state-of-the-art LLMs alongside a domain-specific knowledge base, NextG-GPT
provides context-aware real-time support for researchers, optimizing wireless
network operations. Through a comprehensive evaluation of LLMs, including
Mistral-7B, Mixtral-8x7B, LLaMa3.1-8B, and LLaMa3.1-70B, we demonstrate
significant improvements in answer relevance, contextual accuracy, and overall
correctness. In particular, LLaMa3.1-70B achieves a correctness score of 86.2%
and an answer relevancy rating of 90.6%. By incorporating diverse datasets such
as ORAN-13K-Bench, TeleQnA, TSpec-LLM, and Spec5G, we improve NextG-GPT's
knowledge base, generating precise and contextually aligned responses. This
work establishes a new benchmark in AI-driven support for next-generation
wireless network research, paving the way for future innovations in intelligent
communication systems.
