Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning

Ibrahim Tamim, Sam Aleyadeh, Abdallah Shami
3/3/2023
cs.NIcs.LG

No confident code implementation found for this paper. 3 possible matches can be shown below.

Abstract

The goal of Next-Generation Networks is to improve upon the current networking paradigm, especially in providing higher data rates, near-real-time latencies, and near-perfect quality of service. However, existing radio access network (RAN) architectures lack sufficient flexibility and intelligence to meet those demands. Open RAN (O-RAN) is a promising paradigm for building a virtualized and intelligent RAN architecture. This paper presents a Machine Learning (ML)-based Traffic Steering (TS) scheme to predict network congestion and then proactively steer O-RAN traffic to avoid it and reduce the expected queuing delay. To achieve this, we propose an optimized setup focusing on safeguarding both latency and reliability to serve URLLC applications. The proposed solution consists of a two-tiered ML strategy based on Naive Bayes Classifier and deep Q-learning. Our solution is evaluated against traditional reactive TS approaches that are offered as xApps in O-RAN and shows an average of 15.81 percent decrease in queuing delay across all deployed SFCs.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

87% match2023

Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN

85% match2024

QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning

85% match2025

Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach

85% match1 repo32023

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN

85% match2025

Attention-based Open RAN Slice Management using Deep Reinforcement Learning

83% match2023

Cite this paper

@article{tamim2023intelligent,
  title  = {Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning},
  author = {Ibrahim Tamim and Sam Aleyadeh and Abdallah Shami},
  year   = {2023},
  eprint = {2303.01960},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.01960v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2303.01960.svg)](https://codeofpaper.com/paper/2303.01960)

Discussion