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IEEE/ICACT20260367 Slide.12        [Big Slide]	Oral Presentation
	To solve the proposed optimization problem, we adopt a proactive traffic steering framework that combines deep learning–based prediction with linear programming optimization. A BiLSTM model is used to forecast each RU¡¯s next-slot performance, including throughput, BLER, and resource block usage, by learning temporal patterns from historical network data. These predicted metrics are then fed into a Gurobi-based optimizer, which allocates UE traffic across RUs to maximize throughput while minimizing BLER and ensuring efficient resource utilization. This joint prediction-and-optimization approach enables intelligent, real-time traffic steering in O-RAN systems.

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