Open Radio Access Network (O-RAN) offers an open, programmable architecture for next-generation wireless networks, enabling advanced control through AI-based applications on the near-Real-Time RAN Intelligent Controller (near-RT RIC). However, fully integrated, real-time demonstrations of closed-loop optimization in O-RAN remain scarce. In this paper, we present a complete framework that combines the O-RAN Software Community RIC (OSC RIC) with srsRAN for near-real-time network slicing using Reinforcement Learning (RL). Our system orchestrates resources across diverse slice types (eMBB, URLLC, mMTC) for up to 12 UEs. We incorporate GNU Radio blocks for channel modeling, including Free-Space Path Loss (FSPL), single-tap multipath, AWGN, and Doppler effects, to emulate an urban mobility scenario. Experimental results show that our RL-based xApps dynamically adapt resource allocation and maintain QoS under varying traffic demands, highlighting both the feasibility and challenges of end-to-end AI-driven optimization in a lightweight O-RAN testbed. Our findings establish a baseline for real-time RL-based slicing in a disaggregated 5G framework and underscore the need for further enhancements to support fully simulated PHY digital twins without reliance on commercial software.

翻译：开放无线接入网（O-RAN）为下一代无线网络提供了开放、可编程的架构，通过在近实时无线接入网智能控制器（near-RT RIC）上部署基于人工智能的应用，实现了高级控制。然而，在O-RAN中实现完全集成、实时的闭环优化演示仍然较为罕见。本文提出了一个完整框架，将O-RAN软件社区RIC（OSC RIC）与srsRAN相结合，利用强化学习（RL）实现近实时网络切片。我们的系统为多达12个用户设备（UE）协调多种切片类型（eMBB、URLLC、mMTC）的资源分配。我们集成了GNU Radio模块进行信道建模，包括自由空间路径损耗（FSPL）、单抽头多径效应、加性高斯白噪声（AWGN）和多普勒效应，以模拟城市移动场景。实验结果表明，我们基于强化学习的xApps能够根据变化的流量需求动态调整资源分配并保持服务质量，突显了在轻量级O-RAN测试平台上实现端到端AI驱动优化的可行性与挑战。我们的研究为在解耦的5G框架中实现基于强化学习的实时切片建立了基准，并强调需要进一步改进以支持完全模拟的物理层数字孪生，而无需依赖商业软件。