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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07139-7 |
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| author | Sunita Khichar Yahui Meng Abhishek Sharma Muhammad Saadi Amir Parniarifard Sushank Chaudhary |
| author_facet | Sunita Khichar Yahui Meng Abhishek Sharma Muhammad Saadi Amir Parniarifard Sushank Chaudhary |
| author_sort | Sunita Khichar |
| collection | DOAJ |
| description | 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 limitations in terms of estimation accuracy and computational complexity. To address these challenges, this paper proposes a novel convolutional neural network (CNN)-based channel estimation framework utilizing residual learning and iterative refinement. The residual learning approach mitigates vanishing gradient issues and enhances convergence speed, while iterative refinement progressively improves the channel estimates. Comprehensive simulations using the VehicularA (VehA) channel model demonstrate that the proposed method achieves up to 30% lower mean squared error (MSE) compared to LS estimation and 15% lower MSE compared to ChannelNet at an SNR of 12 dB. Furthermore, the proposed framework shows an MSE reduction of over 20% compared to FSRCNN at low pilot densities, while maintaining robustness across a wide range of SNRs. With its reduced computational complexity and superior performance, the proposed CNN-based framework is suitable for real-time implementation in next-generation wireless systems, including 5G and beyond. |
| format | Article |
| id | doaj-art-d22aa3fd168840528e868ffa337d4196 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d22aa3fd168840528e868ffa337d41962025-08-20T03:38:15ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-07139-7Residual learning based convolution neural network for improved channel estimation for VehA channelSunita Khichar0Yahui Meng1Abhishek Sharma2Muhammad Saadi3Amir Parniarifard4Sushank Chaudhary5Department of Electrical Engineering, Chulalongkorn UniversitySchool of Science, Guangdong University of Petrochemical TechnologyDepartment of Electronics and Communication Engineering, National Institute of TechnologySchool of Science and Technology, Department of Computer Science, Nottingham Trent UniversityGlasgow College, University of Electronic Science and Technology of ChinaSchool of Computer, Guangdong University of Petrochemical TechnologyAbstract 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 limitations in terms of estimation accuracy and computational complexity. To address these challenges, this paper proposes a novel convolutional neural network (CNN)-based channel estimation framework utilizing residual learning and iterative refinement. The residual learning approach mitigates vanishing gradient issues and enhances convergence speed, while iterative refinement progressively improves the channel estimates. Comprehensive simulations using the VehicularA (VehA) channel model demonstrate that the proposed method achieves up to 30% lower mean squared error (MSE) compared to LS estimation and 15% lower MSE compared to ChannelNet at an SNR of 12 dB. Furthermore, the proposed framework shows an MSE reduction of over 20% compared to FSRCNN at low pilot densities, while maintaining robustness across a wide range of SNRs. With its reduced computational complexity and superior performance, the proposed CNN-based framework is suitable for real-time implementation in next-generation wireless systems, including 5G and beyond.https://doi.org/10.1038/s41598-025-07139-7Channel estimationConvolutional neural networkResidual learningDeep learning5G wireless networks |
| spellingShingle | Sunita Khichar Yahui Meng Abhishek Sharma Muhammad Saadi Amir Parniarifard Sushank Chaudhary Residual learning based convolution neural network for improved channel estimation for VehA channel Scientific Reports Channel estimation Convolutional neural network Residual learning Deep learning 5G wireless networks |
| title | Residual learning based convolution neural network for improved channel estimation for VehA channel |
| title_full | Residual learning based convolution neural network for improved channel estimation for VehA channel |
| title_fullStr | Residual learning based convolution neural network for improved channel estimation for VehA channel |
| title_full_unstemmed | Residual learning based convolution neural network for improved channel estimation for VehA channel |
| title_short | Residual learning based convolution neural network for improved channel estimation for VehA channel |
| title_sort | residual learning based convolution neural network for improved channel estimation for veha channel |
| topic | Channel estimation Convolutional neural network Residual learning Deep learning 5G wireless networks |
| url | https://doi.org/10.1038/s41598-025-07139-7 |
| work_keys_str_mv | AT sunitakhichar residuallearningbasedconvolutionneuralnetworkforimprovedchannelestimationforvehachannel AT yahuimeng residuallearningbasedconvolutionneuralnetworkforimprovedchannelestimationforvehachannel AT abhisheksharma residuallearningbasedconvolutionneuralnetworkforimprovedchannelestimationforvehachannel AT muhammadsaadi residuallearningbasedconvolutionneuralnetworkforimprovedchannelestimationforvehachannel AT amirparniarifard residuallearningbasedconvolutionneuralnetworkforimprovedchannelestimationforvehachannel AT sushankchaudhary residuallearningbasedconvolutionneuralnetworkforimprovedchannelestimationforvehachannel |