A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197942" target="_blank" >RIV/00216305:26220/26:0197942 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11008404" target="_blank" >https://ieeexplore.ieee.org/document/11008404</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA65656.2025" target="_blank" >10.1109/RADIOELEKTRONIKA65656.2025</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles

Popis výsledku v původním jazyce

Power Delay Profile (PDP) plays a crucial role in wireless communications, providing information on multipath propagation and signal strength variations over time. Accurate detection of peaks within PDP is essential to identify dominant signal paths, which are critical for tasks such as channel esti mation, localization, and interference management. Traditional approaches to PDP analysis often struggle with noise, low resolution, and the inherent complexity of wireless environments. In this paper, we evaluate the application of traditional and modern deep learning neural networks to reconstruction-based anomaly detection to detect multipath components within the PDP. To further refine detection and robustness, a framework is proposed that combines autoencoders and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. To compare the performance of individual models, a relaxed F1 score strategy is defined. The experimental results show that the proposed framework with transformer-based autoencoder shows superior performance both in terms of reconstruction and anomaly detection.

Název v anglickém jazyce

A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles

Popis výsledku anglicky

Power Delay Profile (PDP) plays a crucial role in wireless communications, providing information on multipath propagation and signal strength variations over time. Accurate detection of peaks within PDP is essential to identify dominant signal paths, which are critical for tasks such as channel esti mation, localization, and interference management. Traditional approaches to PDP analysis often struggle with noise, low resolution, and the inherent complexity of wireless environments. In this paper, we evaluate the application of traditional and modern deep learning neural networks to reconstruction-based anomaly detection to detect multipath components within the PDP. To further refine detection and robustness, a framework is proposed that combines autoencoders and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. To compare the performance of individual models, a relaxed F1 score strategy is defined. The experimental results show that the proposed framework with transformer-based autoencoder shows superior performance both in terms of reconstruction and anomaly detection.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceeding of the 35th International Conference Radioelektronika (RADIOELEKTRONIKA)

ISBN

979-8-3315-4447-8

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

319-323

Název nakladatele

IEEE

Místo vydání

Hnanice, Czech republic

Místo konání akce

Hnanice

Datum konání akce

12. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001509603700030