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Prior Studies on Intelligent Traffic Steering (TS) in O-RAN
Lacava et al. [7]: Propose a programmable ns-O-RAN framework using CNN and Conservative Q-Learning to enable per-user traffic steering and handover decisions based on historical network states, improving real-time adaptability.
Nguyen et al. [8]: Develop a reinforcement learning–based framework that jointly optimizes scheduling, congestion control, and flow splitting in multi-path traffic steering to enhance end-to-end performance under resource constraints.
Tamim et al. [9]: Introduce an ML-based system that predicts congestion by analyzing queue delays and traffic patterns and proactively reroutes traffic to mitigate bottlenecks and latency degradation.
Kavehmadavani et al. [10]: Present a joint intelligent traffic prediction and dynamic resource provisioning (JIFDR) framework that combines ML-based forecasting with real-time resource allocation to avoid under- and over-provisioning in the RAN.
Chatzistefanidis et al. [11]: Apply machine learning to predict user-level QoS metrics for distributed unit (DU) selection, enabling traffic steering toward nodes with lower latency and higher throughput.
Gain et al. [12]: Propose a deep learning–based traffic steering approach that predicts channel quality and link conditions to support adaptive link selection in multi-connectivity environments.
Limitations of Existing Approaches
Depend on indirect indicators (e.g., queue length, congestion level)
Not actively proactive
Limited consideration of real-time physical-layer dynamics
Our Contribution
Direct prediction of next-slot physical-layer metrics:
Throughput
Resource Block (RB) usage
Block Error Rate (BLER)
BiLSTM-based deep learning model for time-series forecasting
Prediction-Aware Traffic Steering via Optimization
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IEEE/ICACT20260367 Slide.07
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