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We propose a deep learning–based proactive traffic steering framework for O-RAN architecture that enhances network throughput and efficiency by forecasting critical radio metrics such as RB usage, throughput, and BLER.
To predict the next time slot metrics, we design a lightweight BiLSTM-based model within the non-RT RIC that accurately forecasts RB usage, throughput, and BLER across RUs. These forecasts are then used to optimize traffic steering decisions.
We formulate a throughput-resource co-optimization problem that uses the predicted KPIs to dynamically allocate UE traffic fractions to the most suitable Rus under a multi-connectivity setup, and we propose Gurobi linear programming to solve it.
Extensive experimental results demonstrate that our approach outperforms achieving up to 13-62% higher throughput, reducing BLER by 30-32%, and improving RB utilization efficiency by 0.20-0.25% compared to Greedy and Equal Split baselines.
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IEEE/ICACT20260367 Slide.06
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