To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

翻译：为实现6G网络智能、可编程且多厂商的无线接入网（RAN），开放RAN（O-RAN）的标准化与开发工作已取得显著进展。然而，目前O-RAN在控制与优化RAN功能方面的适用性尚未得到广泛研究。本文联合优化流量分配、拥塞控制与调度（JFCS），以实现在O-RAN中的智能流量调度应用。通过结合网络效用最大化与随机优化工具，我们提出一种多层优化框架，该框架相较于现有先进方法与基线RAN方法，具有快速收敛、长期效用最优性及显著降低时延的优势。本文主要贡献体现在三个方面：i）提出一种新型JFCS框架，以高效自适应地将流量引导至合适的无线单元；ii）基于强化学习、内逼近与二分搜索方法，开发低复杂度算法，在不同时间尺度上有效求解JFCS问题；iii）通过严格的理论性能分析，证明存在可改善时延与效用优化权衡的缩放因子。综上，本文的研究见解将为构建具备增强控制性与灵活性的全自动化网络铺平道路。数值结果验证了所提算法在收敛速度、长期效用最优性与时延降低方面的有效性。