Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios

Damianos Diasakos; Nikolaos Prodromos; Apostolos Gkamas; Vasileios Kokkinos; Christos Bouras; Philippos Pouyioutas

doi:10.1109/ICUFN65838.2025.11170078

Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios

Damianos Diasakos
, Nikolaos Prodromos
, Apostolos Gkamas
, Vasileios Kokkinos
, Christos Bouras
, Philippos Pouyioutas

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study investigates the prediction of Doppler shift variations in high-speed rail (HSR) environments using advanced deep learning and classical timeseries models. By simulating Doppler shifts at a 5G carrier frequency under noisy conditions, we evaluate and compare the performance of Bidirectional Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, and an optimized Auto-Regressive Integrated Moving Average (ARIMA) model. The results highlight the strengths and limitations of each model, providing a detailed comparison between data-driven and statistical forecasting methods in dynamic 5G communication scenarios.

Original language	English
Title of host publication	ICUFN 2025 - 16th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	150-155
Number of pages	6
ISBN (Electronic)	9798331524876
DOIs	https://doi.org/10.1109/ICUFN65838.2025.11170078
Publication status	Published - 2025
Event	16th International Conference on Ubiquitous and Future Networks, ICUFN 2025 - Hybrid, Lisbon, Portugal Duration: 8 Jul 2025 → 11 Jul 2025

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	16th International Conference on Ubiquitous and Future Networks, ICUFN 2025
Country/Territory	Portugal
City	Hybrid, Lisbon
Period	8/07/25 → 11/07/25

Keywords

5G MIMO
ARIMA
Doppler Shift Prediction
GRU
LSTM
Time-Series Forecasting
Wireless Communication

Access to Document

10.1109/ICUFN65838.2025.11170078

Cite this

Diasakos, D., Prodromos, N., Gkamas, A., Kokkinos, V., Bouras, C., & Pouyioutas, P. (2025). Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios. In ICUFN 2025 - 16th International Conference on Ubiquitous and Future Networks (pp. 150-155). (International Conference on Ubiquitous and Future Networks, ICUFN). IEEE Computer Society. https://doi.org/10.1109/ICUFN65838.2025.11170078

@inproceedings{4ff4c3fdae724b0094c864a0d89389eb,

title = "Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios",

abstract = "This study investigates the prediction of Doppler shift variations in high-speed rail (HSR) environments using advanced deep learning and classical timeseries models. By simulating Doppler shifts at a 5G carrier frequency under noisy conditions, we evaluate and compare the performance of Bidirectional Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, and an optimized Auto-Regressive Integrated Moving Average (ARIMA) model. The results highlight the strengths and limitations of each model, providing a detailed comparison between data-driven and statistical forecasting methods in dynamic 5G communication scenarios.",

keywords = "5G MIMO, ARIMA, Doppler Shift Prediction, GRU, LSTM, Time-Series Forecasting, Wireless Communication",

author = "Damianos Diasakos and Nikolaos Prodromos and Apostolos Gkamas and Vasileios Kokkinos and Christos Bouras and Philippos Pouyioutas",

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year = "2025",

doi = "10.1109/ICUFN65838.2025.11170078",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

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Diasakos, D, Prodromos, N, Gkamas, A, Kokkinos, V, Bouras, C & Pouyioutas, P 2025, Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios. in ICUFN 2025 - 16th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, IEEE Computer Society, pp. 150-155, 16th International Conference on Ubiquitous and Future Networks, ICUFN 2025, Hybrid, Lisbon, Portugal, 8/07/25. https://doi.org/10.1109/ICUFN65838.2025.11170078

Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios. / Diasakos, Damianos; Prodromos, Nikolaos; Gkamas, Apostolos et al.
ICUFN 2025 - 16th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2025. p. 150-155 (International Conference on Ubiquitous and Future Networks, ICUFN).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Diasakos, Damianos

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AU - Gkamas, Apostolos

AU - Kokkinos, Vasileios

AU - Bouras, Christos

AU - Pouyioutas, Philippos

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N2 - This study investigates the prediction of Doppler shift variations in high-speed rail (HSR) environments using advanced deep learning and classical timeseries models. By simulating Doppler shifts at a 5G carrier frequency under noisy conditions, we evaluate and compare the performance of Bidirectional Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, and an optimized Auto-Regressive Integrated Moving Average (ARIMA) model. The results highlight the strengths and limitations of each model, providing a detailed comparison between data-driven and statistical forecasting methods in dynamic 5G communication scenarios.

AB - This study investigates the prediction of Doppler shift variations in high-speed rail (HSR) environments using advanced deep learning and classical timeseries models. By simulating Doppler shifts at a 5G carrier frequency under noisy conditions, we evaluate and compare the performance of Bidirectional Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU) networks, and an optimized Auto-Regressive Integrated Moving Average (ARIMA) model. The results highlight the strengths and limitations of each model, providing a detailed comparison between data-driven and statistical forecasting methods in dynamic 5G communication scenarios.

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KW - LSTM

KW - Time-Series Forecasting

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M3 - Conference contribution

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SP - 150

EP - 155

BT - ICUFN 2025 - 16th International Conference on Ubiquitous and Future Networks

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Diasakos D, Prodromos N, Gkamas A, Kokkinos V, Bouras C, Pouyioutas P. Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios. In ICUFN 2025 - 16th International Conference on Ubiquitous and Future Networks. IEEE Computer Society. 2025. p. 150-155. (International Conference on Ubiquitous and Future Networks, ICUFN). doi: 10.1109/ICUFN65838.2025.11170078

Comparative Analysis for Doppler Shift Prediction in High-Speed 5G Scenarios

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