# Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN

arXiv: https://arxiv.org/abs/2403.10808 · HTML: https://mlsanity.com/paper/2403.10808/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2024.

## Built on by (most cited first, 7 of 7)

- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks with Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2504.00120/) (2025)
- [Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning](https://mlsanity.com/paper/2504.18902/) (2025)
- [Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting](https://mlsanity.com/paper/2502.18046/) (2025)
- [BERTO: Intent-Driven Network Time Series Forecasting via Natural Language Operator Preferences](https://mlsanity.com/paper/2512.05721/) (2025)
- [EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting](https://mlsanity.com/paper/2512.15067/) (2025)
- [Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration](https://mlsanity.com/paper/2606.28274/) (2026)

## Abstract (arXiv)

This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals for Open Radio Access Networks (O-RAN) using a transformer architecture, which is the machine learning model behind generative AI tools. Depending on the anticipated traffic, the system either launches a reinforcement learning-based traffic steering xApp or a cell sleeping rApp to enhance performance metrics like throughput or energy efficiency. Our simulation results demonstrate that the proposed traffic prediction-based network optimization mechanism matches the performance of standalone RAN applications (rApps/ xApps) that are always on during the whole simulation time while offering on-demand activation. This feature is particularly advantageous during instances of abrupt fluctuations in traffic volume. Rather than persistently operating specific applications irrespective of the actual incoming traffic conditions, the proposed prediction-based method increases the average energy efficiency by 39.7% compared to the "Always on Traffic Steering xApp" and achieves 10.1% increase in throughput compared to the "Always on Cell Sleeping rApp". The simulation has been conducted over 24 hours, emulating a whole day traffic pattern for a dense urban area.