LatencyScope: A System-Level Mathematical Framework for 5G RAN Latency

2025-11-26

AI Summary

Existing 5G RAN one-way latency (UL/DL) modeling suffers from low accuracy, difficulty in bottleneck identification, and inability to jointly capture multi-layer dependencies and stochasticity. Method: This paper proposes the first system-level analytical framework that unifies latency modeling across the PHY, MAC, and hardware/software layers; explicitly characterizes scheduling policies, channel randomness, and cross-layer dependencies; and designs a configuration optimization method integrating stochastic optimization and search. Validation is performed on the srsRAN/OAI open-source testbed. Contribution/Results: Compared with MATLAB 5G Toolbox and 5G-LENA, the framework accurately reproduces real-world latency distributions—reducing modeling error by over 40%—and efficiently identifies optimal configurations satisfying URLLC requirements (<1 ms latency at 99.999% reliability). It provides an analytically tractable, scalable theoretical foundation and practical toolset for ultra-low-latency network design.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User model development and evaluation

Abstract

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.

Problem

• Accurately computing one-way latency in 5G RAN across diverse system configurations
• Modeling latency sources across RAN layers while capturing dependencies and stochastic nature
• Finding optimal system configurations meeting URLLC targets through mathematical optimization

Innovation

• Mathematical framework models 5G RAN latency sources
• Configuration optimizer searches billions of system settings
• Validated on open-source testbeds outperforming existing models