Channel Impulse Response Peak Clustering Using Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU151103" target="_blank" >RIV/00216305:26220/23:PU151103 - isvavai.cz</a>
Result on the web
<a href="https://commnet-conf.org/6thEditionProceedings/index.html" target="_blank" >https://commnet-conf.org/6thEditionProceedings/index.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CommNet60167.2023.10365265" target="_blank" >10.1109/CommNet60167.2023.10365265</a>

Result language
angličtina
Original language name
Channel Impulse Response Peak Clustering Using Neural Networks
Original language description
This paper introduces an approach to process channel sounder data acquired from Channel Impulse Response (CIR) of 60GHz and 80GHz channel sounder systems, through the integration of Long Short-Term Memory (LSTM) Neural Network (NN) and Fully Connected Neural Network (FCNN). Theprimary goal is to enhance and automate cluster detection within peaks from noised CIR data. The study initially compares the performance of LSTM NN and FCNN across different input sequence lengths. Notably, LSTM surpasses FCNN due to its incorporation of memory cells, which prove beneficial for handling longer series. Additionally, the paper investigates the robustness of LSTM NN through various architectural configurations. The findings suggest that robust neural networks tend to closely mimic the input function, whereas smaller neural networks are better at generalizing trends in time series data, which is desirable for anomaly detection, where function peaks are regarded as anomalies. Finally, the selected LSTM NN is compared with traditional signal filters, including Butterworth, Savitzky-Golay, Bessel/Thomson, and median filters. Visual observations indicate that the most effective methods for peak detection within channel impulse response data are either the LSTM NN or median filter, as they yield similar results.
Czech name
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Czech description
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Project
<a href="/en/project/GF23-04304L" target="_blank" >GF23-04304L: Multi-band prediction of millimeter-wave propagation effects for dynamic and fixed scenarios in rugged time-varying environments</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2023 6th International Conference on Advanced Communication Technologies and Networking (CommNet)
ISBN
979-8-3503-2939-1
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1-7
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Maroko
Event location
Rabat, Maroko
Event date
Dec 11, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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