Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G

TL;DR

This paper presents a novel AI-driven framework for intent-based management of 6G networks, integrating LLMs, transformers, and decision models to enhance network performance and adaptability.

Contribution

It introduces a three-step AI framework combining fine-tuned LLMs, time series forecasting, and hierarchical decision transformers for efficient 6G network management.

Findings

Improved BERTScore by 6% and semantic similarity by 9%.

Achieved 88% accuracy in intent validation.

Increased throughput by 19.3%, reduced delay by 48.5%, and improved energy efficiency by 54.9%.

Abstract

Intent-driven network management is critical for managing the complexity of 5G and 6G networks. It enables adaptive, on-demand management of the network based on the objectives of the network operators. In this paper, we propose an innovative three-step framework for intent-driven network management based on Generative AI (GenAI) algorithms. First, we fine-tune a Large Language Model (LLM) on a custom dataset using a Quantized Low-Rank Adapter (QLoRA) to enable memory-efficient intent processing within limited computational resources. A Retrieval Augmented Generation (RAG) module is included to support dynamic decision-making. Second, we utilize a transformer architecture for time series forecasting to predict key parameters, such as power consumption, traffic load, and packet drop rate, to facilitate intent validation proactively. Lastly, we introduce a Hierarchical Decision Transformer with Goal Awareness (HDTGA) to optimize the selection and orchestration of network applications and hence, optimize the network. Our intent guidance and processing approach improves BERTScore by 6% and the semantic similarity score by 9% compared to the base LLM model. Again, the proposed predictive intent validation approach can successfully rule out the performance-degrading intents with an average of 88% accuracy. Finally, compared to the baselines, the proposed HDTGA algorithm increases throughput at least by 19.3%, reduces delay by 48.5%, and boosts energy efficiency by 54.9%.