NextG-GPT: Leveraging GenAI for Advancing Wireless Networks and Communication Research

TL;DR

NextG-GPT introduces a novel AI framework combining large language models and domain-specific knowledge to enhance wireless network research with real-time, context-aware support, demonstrating high accuracy and relevance.

Contribution

The paper presents NextG-GPT, a new framework integrating retrieval-augmented generation with LLMs for wireless communication research, improving response relevance and accuracy.

Findings

LLaMa3.1-70B achieves 86.2% correctness.

Answer relevancy rated at 90.6%.

Significant improvements over baseline models.

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.