This paper presents LatencyScope, a mathematical framework for accurately computing one-way latency (for uplink and downlink) in the 5G RAN across diverse system configurations. LatencyScope models latency sources at every layer of the Radio Access Network (RAN), pinpointing system-level bottlenecks--such as radio interfaces, scheduling policies, and hardware/software constraints--while capturing their intricate dependencies and their stochastic nature. LatencyScope also includes a configuration optimizer that uses its mathematical models to search through hundreds of billions of configurations and find settings that meet latency-reliability targets under user constraints. We validate LatencyScope on two open-sourced 5G RAN testbeds (srsRAN and OAI), demonstrating that it can closely match empirical latency distributions and significantly outperform prior analytical models and widely used simulators (MATLAB 5G Toolbox, 5G-LENA). It can also find system configurations that meet Ultra-Reliable Low-Latency Communications (URLLC) targets and enable network operators to efficiently identify the best setup for their systems.

翻译：本文提出LatencyScope，一种用于精确计算5G无线接入网（RAN）在不同系统配置下单向延迟（上行与下行）的数学框架。LatencyScope对无线接入网各层的延迟源进行建模，精准定位系统级瓶颈——如无线接口、调度策略及软硬件约束——同时捕捉其复杂的依赖关系与随机特性。该框架还包含一个配置优化器，利用其数学模型在数千亿种配置中进行搜索，以找到在用户约束条件下满足延迟-可靠性目标的参数设置。我们在两个开源5G RAN测试平台（srsRAN与OAI）上验证了LatencyScope，证明其能够高度贴合实际延迟分布，并显著优于先前的解析模型及广泛使用的仿真工具（MATLAB 5G工具箱、5G-LENA）。该框架还能发现满足超可靠低延迟通信（URLLC）目标的系统配置，帮助网络运营商高效确定其系统的最佳设置方案。