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Optimized CNN-based Channel Estimation for Zero-Padded Uplink OFDMA in 5G New Radio over Fast-Fading Channels

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Optimized CNN-based Channel Estimation for Zero-Padded Uplink OFDMA in 5G New Radio over Fast-Fading Channels

Collection	RMUTK Research Repository (RMUTK IR) วารสารวิชาการ — e-Journal Articles
ID RMUTK Digital	RMUTK000201
Title	Optimized CNN-based Channel Estimation for Zero-Padded Uplink OFDMA in 5G New Radio over Fast-Fading Channels
Alternative title	Optimized CNN-based Channel Estimation for Zero-Padded Uplink OFDMA in 5G New Radio over Fast-Fading Channels
Authors	รศ.ดร.ธนัยรัตน์ มาต๊ะ ผู้แต่งหลัก รศ.ดร.พิสิฐ บุญศรีเมือง (สถาบันเทคโนโลยีพระจอมเกล้าเจ้าคุณทหารลาดกระบัง)
Faculty	คณะวิศวกรรมศาสตร์
Journal Title	Physical Communication
ISSN	1874-4907
Volume / Issue / Pages	Vol.77 \| No.2026 \| pp.103167
Published	2026-05-01
Year	2569
Level	ระดับนานาชาติ (SCOPUS)
Quartile	Q2
DOI	10.1016/j.phycom.2026.103167
Abstract	N/A
Abstract (EN)	This paper addresses a pilot-assisted channel estimation applicable to the uplink orthogonal frequency-division multiple-access with zero-padding in a 5G new radio. The adjacent uplink subchannels in the frequency domain are allocated separately for each user, and each subchannel assigns the pilot signal independently. This paper proposes a convolutional neural network-based channel estimation, including one-dimensional and two-dimensional architectures, designed to optimize the handling of rapid fading channel variations encountered in high-mobility scenarios. The estimation process leverages the subchannels of each user to enhance accuracy. Simulation results demonstrate the effectiveness of the proposed method in offering a better bit-error rate and a higher transmission data rate than the conventional channel estimation methods under challenging conditions. Finally, this paper discusses the considerable computational complexity of aspects of the lightweight two convolutional neural network architectures.
Keywords (TH)	N/A
Keywords (EN)	Uplink OFDMA 5G new radio Channel estimation Convolutional neural network High-mobility scenarios
Access Level	Open Access (เปิดสาธารณะ)

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https://doi.org/10.1016/j.phycom.2026.103167

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