CollabORAN: A Collaborative rApp-xApp-dApp Control Architecture for Fairness-Adaptive Resource Sharing in O-RAN

2026-03-21

AI Summary

This work addresses the challenge of isolated control loops in existing O-RAN systems, which hinder coordinated end-to-end resource allocation and fairness optimization across multiple time scales. To overcome this limitation, the paper proposes CollabORAN, a novel three-tier collaborative architecture integrating rApp, xApp, and dApp layers. CollabORAN enables closed-loop joint optimization across layers and time scales—minute-level policy generation, second-level interference-aware spectrum allocation, and millisecond-level DU scheduling—for the first time. Leveraging a hypergraph-based PRB coloring algorithm, traffic-aware policy generation, and a fast scheduling mechanism, CollabORAN significantly enhances user fairness, effectively mitigates user starvation, and maintains high spectral reuse efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The evolution of Open Radio Access Networks (O-RAN) enables programmable and intelligent control of radio resources through disaggregated architectures and open interfaces. However, existing solutions typically rely on isolated control loops and fail to jointly address end-to-end optimization objectives across multiple timescales. Thus, it remains a key challenge to functionally split optimization algorithms across timescale-specific O-RAN layers while complying with control loop latency specifications. This article proposes CollabORAN, a collaborative rApp-xApp-dApp hierarchical framework for dynamic and equitable spectrum sharing in O-RAN systems. CollabORAN leverages a nested control structure in which the rApp performs traffic-aware policy generation, the xApp executes interference-aware spectrum allocation via hypergraph-based PRB coloring, and the DU-level dApp enforces temporal fairness through fast scheduling. The proposed end-to-end closed-loop design enables coordinated optimization across minutes, seconds, and millisecond time scales. Simulation results demonstrate that CollabORAN significantly improves service fairness and reduces user starvation while maintaining efficient spectrum reuse in dense and dynamic network environments.

Problem

• O-RAN
• resource sharing
• multi-timescale optimization
• fairness
• control loop latency

Innovation

• CollabORAN
• O-RAN
• hypergraph-based PRB coloring
• multi-timescale control
• fairness-aware resource sharing