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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles
Original language description
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.
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceeding of the 35th International Conference Radioelektronika (RADIOELEKTRONIKA)
ISBN
979-8-3315-4447-8
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
319-323
Publisher name
IEEE
Place of publication
Hnanice, Czech republic
Event location
Hnanice
Event date
May 12, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001509603700030

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