Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting

TL;DR

This paper introduces an AI-based latency forecasting system for 5G O-RAN that improves real-time prediction accuracy and scalability, validated through hardware implementation and experimental results.

Contribution

It presents a scalable, real-time latency prediction system using LSTM models integrated into a functional O-RAN prototype, addressing prior scalability and validation issues.

Findings

Achieved a loss metric below 0.04 in latency prediction

Validated system's effectiveness in real-world 5G environments

Demonstrated scalability within an open-source framework

Abstract

The increasing complexity and dynamic nature of 5G open radio access networks (O-RAN) pose significant challenges to maintaining low latency, high throughput, and resource efficiency. While existing methods leverage machine learning for latency prediction and resource management, they often lack real-world scalability and hardware validation. This paper addresses these limitations by presenting an artificial intelligence-driven latency forecasting system integrated into a functional O-RAN prototype. The system uses a bidirectional long short-term memory model to predict latency in real time within a scalable, open-source framework built with FlexRIC. Experimental results demonstrate the model's efficacy, achieving a loss metric below 0.04, thus validating its applicability in dynamic 5G environments.