5G OFDM channel estimation method based on complex-valued generative adversarial network
Accurate channel estimation is a critical component in the design of 5G OFDM communication system receivers, since it can significantly reduce the bit error rate (BER), thus improving wireless communication efficiency and quality. Channel estimation methods based on least square (LS) and minimum mea...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-03-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024069/ |
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| _version_ | 1841533880798019584 |
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| author | LU Yuanzhi WEI Xianglin YU Long YAO Changhua |
| author_facet | LU Yuanzhi WEI Xianglin YU Long YAO Changhua |
| author_sort | LU Yuanzhi |
| collection | DOAJ |
| description | Accurate channel estimation is a critical component in the design of 5G OFDM communication system receivers, since it can significantly reduce the bit error rate (BER), thus improving wireless communication efficiency and quality. Channel estimation methods based on least square (LS) and minimum mean square error (MMSE) effectively utilize the system’s sparsity, but LS algorithms face low computational precision, while MMSE algorithms suffer from high computational complexity. To promote the estimation accuracy, practitioners have presented several deep learning-based channel estimation methods. However, existing methods often split complex matrices into real and imaginary parts, failing to adequately capture the complex characteristics of the channel, leading to distortion in the estimated channel matrix. A complex-valued generative adversarial network (GAN) model that could fully extract the complex features of the signals was proposed, enabling accurate estimation of the channel matrix for the physical downlink shared channel (PDSCH) in the 5G new radio (NR) standard. To validate the effectiveness of the proposed method, the proposed method was compared with LS algorithms, actual channel estimation, super-resolution neural networks, and residual neural network channel estimation methods. Results show that when the mean square error between the estimated channel matrix and the true channel matrix is 0.01, the proposed method-based communication system has a signal-to-noise ratio (SNR) that is 5 dB higher than existing ones. |
| format | Article |
| id | doaj-art-330c2c583834454f9ab326394ad56645 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-330c2c583834454f9ab326394ad566452025-01-15T02:48:19ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-03-01403952550751805G OFDM channel estimation method based on complex-valued generative adversarial networkLU YuanzhiWEI XianglinYU LongYAO ChanghuaAccurate channel estimation is a critical component in the design of 5G OFDM communication system receivers, since it can significantly reduce the bit error rate (BER), thus improving wireless communication efficiency and quality. Channel estimation methods based on least square (LS) and minimum mean square error (MMSE) effectively utilize the system’s sparsity, but LS algorithms face low computational precision, while MMSE algorithms suffer from high computational complexity. To promote the estimation accuracy, practitioners have presented several deep learning-based channel estimation methods. However, existing methods often split complex matrices into real and imaginary parts, failing to adequately capture the complex characteristics of the channel, leading to distortion in the estimated channel matrix. A complex-valued generative adversarial network (GAN) model that could fully extract the complex features of the signals was proposed, enabling accurate estimation of the channel matrix for the physical downlink shared channel (PDSCH) in the 5G new radio (NR) standard. To validate the effectiveness of the proposed method, the proposed method was compared with LS algorithms, actual channel estimation, super-resolution neural networks, and residual neural network channel estimation methods. Results show that when the mean square error between the estimated channel matrix and the true channel matrix is 0.01, the proposed method-based communication system has a signal-to-noise ratio (SNR) that is 5 dB higher than existing ones.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024069/5G new radiochannel estimationPDSCHcomplex valued neural networkgenerative adversarial network |
| spellingShingle | LU Yuanzhi WEI Xianglin YU Long YAO Changhua 5G OFDM channel estimation method based on complex-valued generative adversarial network Dianxin kexue 5G new radio channel estimation PDSCH complex valued neural network generative adversarial network |
| title | 5G OFDM channel estimation method based on complex-valued generative adversarial network |
| title_full | 5G OFDM channel estimation method based on complex-valued generative adversarial network |
| title_fullStr | 5G OFDM channel estimation method based on complex-valued generative adversarial network |
| title_full_unstemmed | 5G OFDM channel estimation method based on complex-valued generative adversarial network |
| title_short | 5G OFDM channel estimation method based on complex-valued generative adversarial network |
| title_sort | 5g ofdm channel estimation method based on complex valued generative adversarial network |
| topic | 5G new radio channel estimation PDSCH complex valued neural network generative adversarial network |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024069/ |
| work_keys_str_mv | AT luyuanzhi 5gofdmchannelestimationmethodbasedoncomplexvaluedgenerativeadversarialnetwork AT weixianglin 5gofdmchannelestimationmethodbasedoncomplexvaluedgenerativeadversarialnetwork AT yulong 5gofdmchannelestimationmethodbasedoncomplexvaluedgenerativeadversarialnetwork AT yaochanghua 5gofdmchannelestimationmethodbasedoncomplexvaluedgenerativeadversarialnetwork |