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

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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Bibliographic Details
Main Authors: Sunita Khichar, Yahui Meng, Abhishek Sharma, Muhammad Saadi, Amir Parniarifard, Sushank Chaudhary
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Channel estimation
Convolutional neural network
Residual learning
Deep learning
5G wireless networks
Online Access:https://doi.org/10.1038/s41598-025-07139-7
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