GAN-based channel estimation for massive MIMO system
As a key technology of 5G, massive MIMO system can significantly improve spectrum efficiency and energy efficiency by equipping a large number of antennas in base stations.However, in massive MIMO system, accurate channel estimation faces severe challenges.In order to estimate the channel accurately...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2021-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021135/ |
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| _version_ | 1841529052570058752 |
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| author | Yuxiu HUA Rongpeng LI Zhifeng ZHAO Jianjun WU Honggang ZHANG |
| author_facet | Yuxiu HUA Rongpeng LI Zhifeng ZHAO Jianjun WU Honggang ZHANG |
| author_sort | Yuxiu HUA |
| collection | DOAJ |
| description | As a key technology of 5G, massive MIMO system can significantly improve spectrum efficiency and energy efficiency by equipping a large number of antennas in base stations.However, in massive MIMO system, accurate channel estimation faces severe challenges.In order to estimate the channel accurately when the pilot sequence length is smaller than the number of transmitting antennas and the channel noise is strong, the estimation method N2N-GAN was proposed.N2N-GAN firstly denoised the pilot channel at the receiving end, and then used the conditional generative adversarial network to estimate the channel matrix according to the denoised pilot signal.Simulation experiments show that N2N-GAN achieves better robustness against noise compared with traditional channel estimation algorithms and deep learning-based methods.Meanwhile, it can adapt to scenarios with fewer pilot symbols and more antennas. |
| format | Article |
| id | doaj-art-d492ad8d852f44b1be62217c20a282c4 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-d492ad8d852f44b1be62217c20a282c42025-01-15T03:26:22ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-06-0137142259808358GAN-based channel estimation for massive MIMO systemYuxiu HUARongpeng LIZhifeng ZHAOJianjun WUHonggang ZHANGAs a key technology of 5G, massive MIMO system can significantly improve spectrum efficiency and energy efficiency by equipping a large number of antennas in base stations.However, in massive MIMO system, accurate channel estimation faces severe challenges.In order to estimate the channel accurately when the pilot sequence length is smaller than the number of transmitting antennas and the channel noise is strong, the estimation method N2N-GAN was proposed.N2N-GAN firstly denoised the pilot channel at the receiving end, and then used the conditional generative adversarial network to estimate the channel matrix according to the denoised pilot signal.Simulation experiments show that N2N-GAN achieves better robustness against noise compared with traditional channel estimation algorithms and deep learning-based methods.Meanwhile, it can adapt to scenarios with fewer pilot symbols and more antennas.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021135/channel estimationmassive MIMOsignaldenoisinggenerative adversarial network |
| spellingShingle | Yuxiu HUA Rongpeng LI Zhifeng ZHAO Jianjun WU Honggang ZHANG GAN-based channel estimation for massive MIMO system Dianxin kexue channel estimation massive MIMO signaldenoising generative adversarial network |
| title | GAN-based channel estimation for massive MIMO system |
| title_full | GAN-based channel estimation for massive MIMO system |
| title_fullStr | GAN-based channel estimation for massive MIMO system |
| title_full_unstemmed | GAN-based channel estimation for massive MIMO system |
| title_short | GAN-based channel estimation for massive MIMO system |
| title_sort | gan based channel estimation for massive mimo system |
| topic | channel estimation massive MIMO signaldenoising generative adversarial network |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021135/ |
| work_keys_str_mv | AT yuxiuhua ganbasedchannelestimationformassivemimosystem AT rongpengli ganbasedchannelestimationformassivemimosystem AT zhifengzhao ganbasedchannelestimationformassivemimosystem AT jianjunwu ganbasedchannelestimationformassivemimosystem AT honggangzhang ganbasedchannelestimationformassivemimosystem |