# How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?

arXiv: https://arxiv.org/abs/2309.00489 · HTML: https://mlsanity.com/paper/2309.00489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Virtual Reality Gaming on the Cloud: A Reality Check](https://mlsanity.com/paper/2109.10114/) (2021)
- [Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control](https://mlsanity.com/paper/2101.08391/) (2021)
- [Artificial Intelligence in Open Radio Access Network](https://mlsanity.com/paper/2104.09445/) (2021)
- [Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks](https://mlsanity.com/paper/2209.13532/) (2022)

## Built on by (most cited first, 2 of 2)

- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12753/) (2025)

## Abstract (arXiv)

The success of immersive applications such as virtual reality (VR) gaming and metaverse services depends on low latency and reliable connectivity. To provide seamless user experiences, the open radio access network (O-RAN) architecture and 6G networks are expected to play a crucial role. RAN slicing, a critical component of the O-RAN paradigm, enables network resources to be allocated based on the needs of immersive services, creating multiple virtual networks on a single physical infrastructure. In the O-RAN literature, deep reinforcement learning (DRL) algorithms are commonly used to optimize resource allocation. However, the practical adoption of DRL in live deployments has been sluggish. This is primarily due to the slow convergence and performance instabilities suffered by the DRL agents both upon initial deployment and when there are significant changes in network conditions. In this paper, we investigate the impact of time series forecasting of traffic demands on the convergence of the DRL-based slicing agents. For that, we conduct an exhaustive experiment that supports multiple services including real VR gaming traffic. We then propose a novel forecasting-aided DRL approach and its respective O-RAN practical deployment workflow to enhance DRL convergence. Our approach shows up to 22.8%, 86.3%, and 300% improvements in the average initial reward value, convergence rate, and number of converged scenarios respectively, enhancing the generalizability of the DRL agents compared with the implemented baselines. The results also indicate that our approach is robust against forecasting errors and that forecasting models do not have to be ideal.