LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning

TL;DR

This paper proposes an intent-based network automation framework for O-RAN that combines LLM processing, intent validation, and hierarchical reinforcement learning for network optimization, leading to significant performance improvements.

Contribution

It introduces a novel integration of LLMs, intent validation, and attention-based hierarchical reinforcement learning for efficient network management in O-RAN.

Findings

At least 12% increase in throughput

17.1% improvement in energy efficiency

26.5% reduction in network delay

Abstract

Intent-based network automation is a promising tool to enable easier network management however certain challenges need to be effectively addressed. These are: 1) processing intents, i.e., identification of logic and necessary parameters to fulfill an intent, 2) validating an intent to align it with current network status, and 3) satisfying intents via network optimizing functions like xApps and rApps in O-RAN. This paper addresses these points via a three-fold strategy to introduce intent-based automation for O-RAN. First, intents are processed via a lightweight Large Language Model (LLM). Secondly, once an intent is processed, it is validated against future incoming traffic volume profiles (high or low). Finally, a series of network optimization applications (rApps and xApps) have been developed. With their machine learning-based functionalities, they can improve certain key performance indicators such as throughput, delay, and energy efficiency. In this final stage, using an attention-based hierarchical reinforcement learning algorithm, these applications are optimally initiated to satisfy the intent of an operator. Our simulations show that the proposed method can achieve at least 12% increase in throughput, 17.1% increase in energy efficiency, and 26.5% decrease in network delay compared to the baseline algorithms.