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In this study, we propose a deep learning–based intelligent proactive traffic steering framework tailored for O-RAN environments.
The framework formulates a throughput-resource co-optimization problem aimed at enhancing network efficiency by leveraging predicted required RBs, throughput, and BLER for each RU in the upcoming time slot.
To achieve the forecasting outcome, a BiLSTM-based forecasting model is deployed in the non-real-time RIC to estimate key radio metrics proactively.
These predictions are then used by the optimization module, which employs linear programming via Gurobi to dynamically allocate user traffic to RUs. The objective is to maximize throughput and resource utilization while minimizing the BLER.
The experimental evaluation, conducted using real O-RAN traffic traces, demonstrates that the proposed approach achieves approximately up to 13% and 62% higher throughput compared to the Greedy and Equal Split baselines, respectively.
Additionally, it reduces the BLER by approximately 30–32%, and improves RBs utilization efficiency by 0.20–0.25% over these baselines. These results highlight the effectiveness of our framework in delivering adaptive, efficient, and reliable traffic management for next-generation disaggregated RAN systems.
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IEEE/ICACT20260367 Slide.19
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