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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Deep learning-based high-speed railway communication systems.

Do Viet Ha¹, Trinh Van Chien², Hien Quoc Ngo³

¹University of Transport and Communications, Hanoi, Vietnam.

Scientific Reports

|April 9, 2026

Summary

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This study introduces two data-driven frameworks using deep neural networks (DNNs) to improve Long-Term Evolution for Railways (LTE-R) communication quality. These methods effectively reduce errors caused by high-speed railway conditions.

Area of Science:

Wireless Communication Systems
Signal Processing
Machine Learning

Background:

High-speed railway wireless communication faces challenges from Doppler shifts and multipath fading.
Reliable connectivity in Long-Term Evolution for Railways (LTE-R) is difficult under dynamic conditions.

Purpose of the Study:

To enhance communication quality and reduce error probability in high-mobility LTE-R scenarios.
To investigate data-driven frameworks for improved channel estimation and signal processing.

Main Methods:

A deep neural network (DNN) framework was developed to learn channel behavior, bypassing traditional time-domain channel estimation (TDCE) limitations.
An autoencoder/decoder architecture was employed to replace conventional processing units and extract prior information from data.

Keywords:

Autoencoder Channel estimation Deep learning High-speed train communications

Related Experiment Videos

Last Updated: May 6, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Main Results:

The proposed DNN-based frameworks demonstrated superior tracking of rapid channel fluctuations compared to conventional methods.
These frameworks effectively mitigated Doppler-induced distortions, leading to improved detection performance in high-mobility LTE-R environments.

Conclusions:

Learning-based signal processing holds significant potential for enhancing the reliability and efficiency of high-speed railway communications.
The developed frameworks offer a promising approach to overcome the challenges of dynamic wireless propagation conditions in LTE-R systems.