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In this paper, we present a proactive deep learning framework for traffic steering in O-RAN, using next-slot predictions of throughput, RB usage, and BLER.
A BiLSTM model running in the non-real-time RAN intelligent controller (non-RT-RIC) forecasts these indicators to enable adaptive traffic allocation.
We formulate a prediction-informed throughput resource co-optimization problem that maximizes throughput, enhances RB efficiency, and reduces BLER by intelligently distributing traffic across multiple RUs.
We then propose a Gurobi-based linear programming to obtain a near-optimal allocation matrix. The proposed prediction and optimization framework is lightweight, scalable, and deployable within the non-RT-RIC.
Experimental evaluations demonstrate that our proposed method significantly outperforms traditional equal-split and greedy baselines, achieving up to 13-62% higher throughput, 30–32% lower BLER, and 0.20–0.25% better RB utilization efficiency.
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IEEE/ICACT20260367 Slide.05
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