CollabORAN: A Collaborative rApp-xApp-dApp Control Architecture for Fairness-Adaptive Resource Sharing in O-RAN
This addresses the problem of fair and efficient resource allocation in O-RAN systems for network operators and users, representing an incremental advancement over existing isolated control solutions.
The paper tackled the challenge of jointly optimizing radio resource sharing across multiple timescales in Open Radio Access Networks (O-RAN) by proposing CollabORAN, a collaborative hierarchical framework, which improved service fairness and reduced user starvation while maintaining efficient spectrum reuse in simulations.
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.