Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning
This addresses the need for flexible and intelligent radio access networks to meet high data rate and low latency demands, though it appears incremental as it builds on existing O-RAN paradigms with a hybrid ML method.
The paper tackled the problem of network congestion in Open RAN (O-RAN) for URLLC applications by proposing a machine learning-based traffic steering scheme, resulting in an average 15.81% decrease in queuing delay compared to traditional reactive approaches.
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.