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

arXiv: https://arxiv.org/abs/2502.18046 · HTML: https://mlsanity.com/paper/2502.18046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Prediction](https://mlsanity.com/paper/2210.08829/) (2022)
- [Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN](https://mlsanity.com/paper/2403.10808/) (2024)

## Abstract (arXiv)

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.