# Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2401.06922 · HTML: https://mlsanity.com/paper/2401.06922/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [Predictive Closed-Loop Service Automation in O-RAN based Network Slicing](https://mlsanity.com/paper/2202.01966/) (2022)
- [Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Prediction](https://mlsanity.com/paper/2210.08829/) (2022)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications](https://mlsanity.com/paper/2306.16481/) (2023)
- [Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks](https://mlsanity.com/paper/2111.12064/) (2021)
- [Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul](https://mlsanity.com/paper/2211.07466/) (2022)
- [Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios](https://mlsanity.com/paper/2302.04108/) (2023)
- [Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices](https://mlsanity.com/paper/2307.03868/) (2023)

## Built on by (most cited first, 5 of 5)

- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)

## Abstract (arXiv)

With emerging applications such as autonomous driving, smart cities, and smart factories, network slicing has become an essential component of 5G and beyond networks as a means of catering to a service-aware network. However, managing different network slices while maintaining quality of services (QoS) is a challenge in a dynamic environment. To address this issue, this paper leverages the heterogeneous experiences of distributed units (DUs) in ORAN systems and introduces a novel approach to ORAN slicing xApp using distributed deep reinforcement learning (DDRL). Additionally, to enhance the decision-making performance of the RL agent, a prediction rApp based on long short-term memory (LSTM) is incorporated to provide additional information from the dynamic environment to the xApp. Simulation results demonstrate significant improvements in network performance, particularly in reducing QoS violations. This emphasizes the importance of using the prediction rApp and distributed actors' information jointly as part of a dynamic xApp.