Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Distributed SearchConstraint Satisfaction and Optimization: Distributed CSP/Optimization
CollabORAN: A Collaborative rApp-xApp-dApp Control Architecture for Fairness-Adaptive Resource Sharing in O-RAN
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
O-RAN
resource sharing
multi-timescale optimization
fairness
control loop latency
Innovation
Methods, ideas, or system contributions that make the work stand out.
CollabORAN
O-RAN
hypergraph-based PRB coloring
multi-timescale control
fairness-aware resource sharing
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