Interoperable rApp/xApp Control over O-RAN for Mobility-aware Dynamic Spectrum Allocation

TL;DR

The paper tackles mobility-aware dynamic spectrum allocation in O-RAN by linking long-term traffic intelligence at the Non-RT RIC with near-real-time, graph-based PRB coloring at the Near-RT RIC. It introduces a graph-theoretic DSA framework where a traffic-predictive rApp outputs high-level policies that guide a DSA-xApp, which constructs conflict graphs, performs PRB coloring, and applies a conflict-aware MPF to ensure fairness and prevent starvation. Evaluation in a multi-cell setup shows PRB assignment success above 90% and service-share fairness above 85%, with graph-coloring–based allocation and MPF driving substantial gains across diverse traffic and interference conditions. The approach adheres to O-RAN principles, offering a modular, vendor-agnostic solution and a foundation for future enhancements such as learning-enabled coloring, energy-aware optimization, and broader cross-domain orchestration in 6G networks.

Abstract

Open Radio Access Networks (O-RAN) enable the disaggregation of radio access functions and the deployment of control applications across different timescales. However, designing interoperable control schemes that jointly exploit long-term traffic awareness and near-real-time radio resource optimization remains a challenging problem, particularly under dense multi-cell interference and heterogeneous service demands. This paper proposes an interoperable rApp/xApp-driven dynamic spectrum allocation (DSA) framework for O-RAN, based on a graph-theoretic formulation of physical resource block (PRB) assignment. The proposed architecture leverages a non-real-time radio intelligent controller (Non-RT RIC) rApp to predict aggregated traffic evolution and generate high-level spectrum policies at the minutes timescale, while a near-real-time RIC (Near-RT RIC) xApp constructs a user-centric conflict graph and performs fairness-aware PRB allocation at sub-second timescales. To mitigate persistent user starvation, a conflict-aware modified proportional fair (MPF) scheduling mechanism is applied, enabling controlled interference-free PRB time-sharing. Extensive simulation results demonstrate that the proposed framework significantly improves the PRB assignment success rate (above 90%) and service-share fairness (above 85%) across different channel configurations and user demands, while maintaining architectural separation and rApp/xApp interoperability in accordance with O-RAN principles.

Figures (6)

• Figure 1: Interoperable rApp/xApp functionalities and heterogeneous O-RAN architecture with multiple co-operating O-RUs.
• Figure 2: Graph-based continuous optimization cycle of DSA-xApp.
• Figure 3: Sequence diagram for closed-loop rApp/xApp-driven DSA workflow.
• Figure 4: rApp training and inference performance. (a) Traffic prediction final MSE as a function of lookback window $L$ for different learning rates $a$. (b) Actual vs predicted traffic load during the testing (last) day. Decision areas for different selected numerology $\mu$ are separated with the red dashed lines.
• Figure 5: xApp inference performance. (a) Success rate (%) and (b) Jain's fairness service-share (%) as a function of the selected numerology $\mu$ for different UE demands $d_u$.