ArXiv

Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance

2026-05-12 · 17:43 UTC ·Mannam Veera Narayana, Rohit Singh, Deepa M. R... ·1 min read
Authors
Mannam Veera Narayana, Rohit Singh, Deepa M. R...
Categories
eess.SP, cs.AI, cs.DB, cs.LG, cs.NI
arXiv
https://arxiv.org/abs/2605.12453v1
PDF
https://arxiv.org/pdf/2605.12453v1

Brief

The paper presents a real-world dataset aimed at enabling AI-native mobility in 6G by replacing common simulation-based data with measurements from a commercial network. To address high interruption times and measurement overhead during UE mobility, the authors collected multi-speed traces across pedestrian, bike, car, bus, and train scenarios, emphasizing handover events. A key contribution is inclusion of timing advance (TA) at RACH trigger, MAC CE, and PDCCH grant events. The authors provide dataset generation details and exploratory analyses and propose use cases such as TA prediction and AI/ML-driven beam and handover management (arXiv:2605.12453v1).

Why it matters

On 2026-05-12, Mannam Veera Narayana, Rohit Singh, Deepa M. R, and Radha Krishna Ganti published a real-world mobility dataset (arXiv:2605.12453v1) collected from a commercially deployed network across five mobility modes — pedestrian, bike, car, bus, and train — and multiple speeds, with primary focus on handover (HO) scenarios to reduce HO interruption time and preserve throughput.

Key details

  • The dataset uniquely includes timing advance (TA) measurements tied to signaling events (RACH trigger, MAC CE, and PDCCH grant) and is intended to support AI/ML tasks such as TA prediction, beam management, and mobility/handover model training; the paper describes dataset creation, experimental setup, data acquisition/extraction, and exploratory analyses on mobility, beam management, and TA.
Source evidence

Abstract

To address the issues of high interruption time and measurement report overhead under user equipment (UE) mobility especially in high speed 5G use cases the use of AI/ML techniques (AI/ML beam management and mobility procedures) have been proposed. These techniques rely heavily on data that are most often simulated for various scenarios and do not accurately reflect real deployment behavior or user traffic patterns. Therefore, there is an utmost need for realistic datasets under various conditions. This work presents a dataset collected from a commercially deployed network across various modes of mobility (pedestrian, bike, car, bus, and train) and at multiple speeds to depict real time UE mobility. When collecting the dataset, we focused primarily on handover (HO) scenarios, with the aim of reducing the HO interruption time and maintaining continuous throughput during and immediately after HO execution. To support this research, the dataset includes timing advance (TA) measurements at various signaling events such as RACH trigger, MAC CE, and PDCCH grant which are typically missing in existing works. We cover a detailed description of the creation of the dataset; experimental setup, data acquisition, and extraction. We also cover an exploratory analysis of the data, with a primary focus on mobility, beam management, and TA. We discuss multiple use cases in which the proposed dataset can facilitate understanding of the inference of the AI/ML model. One such use case is to train and evaluate various AI/ML models for TA prediction.