# Time Synchronization in 5G Wireless Edge: Requirements and Solutions for Critical-MTC

arXiv: https://arxiv.org/abs/1906.06380 · HTML: https://mlsanity.com/paper/1906.06380/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning](https://mlsanity.com/paper/2201.09531/) (2022)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing](https://mlsanity.com/paper/2512.03719/) (2025)
- [Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://mlsanity.com/paper/2302.08447/) (2023)

## Abstract (arXiv)

Wireless edge is about distributing intelligence to the wireless devices wherein the distribution of accurate time reference is essential for time-critical machine-type communication (cMTC). In 5G-based cMTC, enabling time synchronization in the wireless edge means moving beyond the current synchronization needs and solutions in 5G radio access. In this article, we analyze the device-level synchronization needs of potential cMTC applications: industrial automation, power distribution, vehicular communication, and live audio/video production. We present an over-the-air (OTA) synchronization scheme comprised of 5G air interface parameters, and discuss their associated timing errors. We evaluate the estimation error in device-to-base station propagation delay from timing advance (TA) under random errors and show how to reduce the estimation error. In the end, we identify the random errors specific to dense multipath fading environments and discuss countermeasures.