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.

翻译：开放无线接入网（O-RAN）的演进通过解耦架构与开放接口实现了无线资源的可编程与智能化管控。然而现有方案通常依赖孤立控制环路，无法联合实现跨多时间尺度的端到端优化目标。因此，如何在满足控制环路时延规范的前提下，将优化算法按时间尺度特征拆分至不同O-RAN层仍是关键挑战。本文提出CollabORAN——一种用于O-RAN系统动态公平频谱共享的rApp-xApp-dApp协作式层次化框架。该框架采用嵌套控制结构：rApp执行流量感知策略生成，xApp通过基于超图的PRB着色实现干扰感知频谱分配，DU级dApp则通过快速调度强制执行时间公平性。所提出的端到端闭环设计能够协同优化分钟级、秒级与毫秒级时间尺度。仿真结果表明，在密集动态网络环境下，CollabORAN在维持高效频谱复用能力的同时，显著提升服务公平性并降低用户饥饿现象。