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The proposed algorithm performs intelligent, proactive traffic steering in O-RAN by combining BiLSTM-based prediction with linear programming optimization. For each RU, a sequence of historical features is processed through a BiLSTM with attention pooling to predict next-slot metrics, including throughput, BLER, and RB usage. These predictions are used to compute utility coefficients for each RU–UE pair, and a Gurobi-based linear program is formulated to optimally allocate UE traffic across RUs while respecting capacity, reliability, and throughput constraints. The output is an optimal traffic allocation matrix that adapts in real time to predicted network conditions.
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IEEE/ICACT20260367 Slide.14
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