Residual learning based convolution neural network for improved channel estimation for VehA channel

Abstract Accurate channel estimation is crucial for reliable communication in orthogonal frequency division multiplexing (OFDM) systems, especially in high-mobility scenarios. Traditional channel estimation techniques, such as least squares (LS) and linear minimum mean square error (LMMSE), face lim...

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Главные авторы:	Sunita Khichar, Yahui Meng, Abhishek Sharma, Muhammad Saadi, Amir Parniarifard, Sushank Chaudhary
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2025-07-01
Серии:	Scientific Reports
Предметы:	Channel estimation Convolutional neural network Residual learning Deep learning 5G wireless networks
Online-ссылка:	https://doi.org/10.1038/s41598-025-07139-7
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