Fifth-generation (5G) systems are increasingly studied as shared communication and computing infrastructure for connected vehicles, roadside edge platforms, and future unmanned-system applications. Yet results from simulators, host-OS emulators, digital twins, and hardware-in-the-loop testbeds are often compared as if timing, input/output (I/O), and control-loop behavior were equivalent across them. They are not. Consequently, apparent limits in throughput, latency, scalability, or real-time behavior may reflect the execution harness rather than the wireless design itself. This paper presents \textit{AtlasRAN}, a capability-oriented framework for modeling and performance evaluation of 5G Open Radio Access Network (O-RAN) platforms. It introduces two reference architectures, terminology that separates functional compatibility from timing fidelity, and a capability matrix that maps research questions to evaluation environments that can support them credibly. O-RAN is used here as an experimental coordinate system spanning Centralized Unit (CU)/Distributed Unit (DU) partitioning, fronthaul transport, control exposure, and core-network anchoring. We validate \textit{AtlasRAN} through a CU-DU uplink load study on a coherent CPU-GPU edge platform. For both a CPU-only baseline and a GPU-accelerated low-density parity-check decoding variant, aggregate goodput drops sharply as user count rises from 1 to 12, while fairness remains near ideal and compute utilization decreases rather than increases. This pattern indicates time-scale dilation and online I/O starvation in the emulation harness, not decoder saturation, as the dominant scaling limit. The key lesson is that timing, memory, and transport semantics must be reported as first-class experimental variables when evaluating ubiquitous 5G infrastructure.

翻译：第五代（5G）系统正被越来越多地研究作为网联车辆、路边边缘平台及未来无人系统应用的共享通信与计算基础设施。然而，来自仿真器、主机操作系统模拟器、数字孪生以及硬件在环测试平台的结果常常被直接对比，仿佛其时序、输入/输出（I/O）及控制回路行为均具有等效性——但事实并非如此。因此，吞吐量、时延、可扩展性或实时行为中的表面限制可能反映的是执行框架本身的局限，而非无线设计的问题。本文提出\textit{AtlasRAN}，一个面向5G开放无线接入网（O-RAN）平台建模与性能评估的能力导向框架。该框架引入两种参考架构、一套将功能兼容性与时序保真度分离的术语体系，以及一个将研究问题映射至可为其提供可信支持评估环境的能力矩阵。本文以O-RAN作为实验坐标系，涵盖集中单元（CU）/分布单元（DU）划分、前传传输、控制开放及核心网锚定。我们通过在一套同构CPU-GPU边缘平台上开展的CU-DU上行负载研究对\textit{AtlasRAN}进行验证。在纯CPU基线配置与GPU加速低密度奇偶校验解码变体两种场景下，当用户数从1增加至12时，聚合有效吞吐量急剧下降，而公平性近乎理想，计算利用率不升反降。这一模式表明：主导扩展瓶颈并非解码器饱和，而是仿真框架中的时间尺度扩张与在线I/O匮乏。核心启示在于：评估泛在5G基础设施时，必须将时序、内存及传输语义作为首要实验变量进行报告。