Bottleneck-Based Deep Learning-Driven Resource Allocation in O-RAN

With increasing demands for ultra-reliable, low-latency applications and next-generation network services, integrating artificial intelligence and machine learning (AI/ML) into Open Radio Access Network (O-RAN) components has become a critical research focus. However, realizing the full potential of AI/ML in O-RAN presents unresolved challenges due to the absence of system-level mechanisms for dynamic resource allocation and limited coordination among the functionally separated components. The paper addresses some of these challenges by proposing a bottleneck-based deep learning-driven resource allocation approach that employs a Gated Recurrent Unit (GRU)-based forecasting model to proactively identify and mitigate bottleneck resources, enabling the system to adapt to fluctuating user demands and varying network conditions, and guiding task reallocation through policy-driven decisions. Our approach combines the capabilities of the Non-Real-Time (Non-RT) and Near-Real-Time (Near-RT) RAN Intelligent Controllers (RICs) across the cloud-edge continuum. Since edge computing nodes often have limited resources and are more expensive compared to cloud infrastructure, components of the Near-RT RIC are deployed at the edge, while Non-RT RIC components are placed in the cloud. We implement this framework in both xApp and rApp forms, fully compliant with O-RAN specifications, and conduct extensive performance evaluations using real-world network data in an extended Kubernetes environment, demonstrating the integration of Near-RT RIC at the edge and Non-RT RIC in the cloud. Comprehensive performance evaluations conducted on the O-RAN Software Community (OSC) testbed demonstrate significant improvements in network efficiency, scalability, and latency, as the proposed approach significantly outperforms existing methods by reducing resource utilization by 14%-40%, reducing task delay by 21.6%-44.0%, and achieving an admittance ratio improvement ranging from 6.48% to 16.6% compared to other approaches.