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FACTS & FIGURES

JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 3
Current issue: Feb 2025
Next issue: Jun 2025
Avg review time: 84 days
Avg accept to publ: 60 days
APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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FEATURED ARTICLE

A Proposed Signal Reconstruction Algorithm over Bandlimited Channels for Wireless Communications, ASHOUR, A., KHALAF, A., HUSSEIN, A., HAMED, H., RAMADAN, A.
Issue 1/2023

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LATEST NEWS

2025-May-01
Starting from 2025, our Journal will appear 3 times a year. Issues will be published in February, June, and October.

2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

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  1/2025 - 7

Deep Learning Based Channel Estimation for UAVs: A Modified U-Net Approach

GUPTA, C. See more information about GUPTA, C. on SCOPUS See more information about GUPTA, C. on IEEExplore See more information about GUPTA, C. on Web of Science, YADAV, S. S. See more information about YADAV, S. S. on SCOPUS See more information about YADAV, S. S. on SCOPUS See more information about YADAV, S. S. on Web of Science
 
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Download PDF pdficon (1,595 KB) | Citation | Downloads: 472 | Views: 623

Author keywords
channel estimation, machine learning, neural networks, OFDM, unmanned aerial vehicle

References keywords
communications(16), channel(12), ofdm(10), estimation(10), deep(10), communication(10), systems(9), learning(7), networks(6), letters(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2025-02-28
Volume 25, Issue 1, Year 2025, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2025.01007
SCOPUS ID: 86000354176

Abstract
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A stable and reliable communication link is crucial for unmanned aerial vehicle (UAV) applications. Key challenges include the UAV's high mobility (10-100 km/h) and an unstable data link. Orthogonal frequency division multiplexing (OFDM) enables higher data rate transmission with improved bandwidth efficiency, while minimizing channel effects on the received signal and enhancing bit error rate (BER) performance. This article proposes a deep learning based channel estimation (CE) for 802.11ac OFDM systems considering the mobility of the receiver. The proposed CE algorithm is a two-step process. The first step uses an especially developed deep neural network built on the U-Net model for denoising the signal received, followed by least squares (LS) estimation in the next step. The simulation results show that the proposed model has improved the BER by 50% and 40%, the data rate by 10% and 7% and outage probability by 10% and 7%, respectively, when compared to the conventional LS estimator and machine learning based LS estimator. The proposed model has also been evaluated for three different modulation schemes, i.e., QPSK, 16-QAM, and 64-QAM and the complexity analysis has been done to strengthen our studies further.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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