Rural Handover Parameter Tuning to Achieve End to End Latency Requirements of Future Railway Mobile Communication Systems

TL;DR

This study analyzes how L3 handover impacts FRMCS end-to-end latency and reliability in a high-speed rural rail setting. Using a system-level simulator, it sweeps A3 Event Offset $O$ and Time-to-Trigger $TTT$ to quantify effects on the 99.9th percentile latency and reliability across FRMCS scenarios, focusing on a rural 8 km inter-site distance with trains at 500 km/h. The findings show that baseline handover can meet Standard Data Communication requirements, and mobility-parameter tuning can reduce latency by up to ~18.5%, but other FRMCS use cases require additional mobility techniques such as RACH-less LTM or DAPS, or deployment changes like cell densification or 1900 MHz coexistence. The work provides concrete guidance on where mobility improvements are most impactful and outlines practical directions for expanding FRMCS reliability under mobility.

Abstract

GSM-R (GSM for Railways) is a 2G-based standardized ground-to-train communications system that enabled interoperability across different countries. However, as a 2G-based system, it is nearing its lifetime and therefore, it will be replaced with 5G-based Future Railway Mobile Communications System (FRMCS). FRMCS is expected to bring in new use cases that demand low latency and high reliability. However, from a mobility perspective, it is not clear how the low latency and high reliability will be achieved. This paper investigates the effect of handover procedure on latency and reliability and analyzes which use cases of FRMCS can be satisfied using baseline handover. We also sweep through different handover parameter configurations and analyze their effect on mobility performance. Then, we analyze the effect of mobility performance on packet latency and reliability. Our results show that, with baseline handover, Standard Data Communications Scenario is met and optimizing for baseline handover performance can reduce latency by up to 18.5%, indicating that optimizing for mobility performance is crucial in FRMCS.

Figures (8)

• Figure 1: Baseline Handover Procedure.
• Figure 2: Time spent between the reception of Handover Command and the transmission of the first PRACH Preamble.
• Figure 3: Schematic of the railway track used in the simulation studies.
• Figure 4: $99.9$th percentile of the latency distribution for different A3 Event Offset (O) and TTT configurations.
• Figure 5: Total number of handovers vs. different A3 Event Offset (O) and TTT configurations.