User Classification and Traffic Steering in O-RAN

Abstract

The O-RAN architectural framework enables the application of AI/ML techniques for traffic steering and load balancing. Indeed, an effective steering technique is crucial to avoiding ping-pong and radio link failure. Limited observability and network complexity make it challenging to understand individual user needs. Consequently, traffic steering methods struggle to make optimal decisions, resulting in performance degradation due to unnecessary handovers. Motivated by this, we present an xApp for the RAN intelligence controller (RIC) for user equipment (UE) steering to ensure an even load distribution among cells while maintaining an acceptable throughput level. We propose an ML-aided traffic steering technique. The proposed method comprises three phases: UE classification, downlink (DL) throughput prediction, and a traffic steering (TS) technique. A support vector machine (SVM) is used for UE classification, followed by cell throughput prediction using ensemble Long Short-Term Memory (E-LSTM). The TS algorithm uses the information from the ML models to initiate handovers (HO). The SVM model identifies UEs with low throughput, while the E-LSTM predicts cell DL throughput to provide information about potential target cells for these UEs. Experimental results demonstrate that the proposed method achieves an even load distribution of UEs in 60.25% of the cells with few handovers, while also significantly improving UE throughput.

Keywords

• Bagging
• DL throughput
• load balancing
• LSTM
• O-RAN
• supervised learning