Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning
There exist intrinsic inter-carrier interference and inter-subsymbol interference in generalized frequency division multiplexing (GFDM) systems.Under condition of time-frequency doubly selective channels, severe effects of pilot contamination would occur and lead to significant performance degradati...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-10-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021188/ |
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| _version_ | 1841539177209921536 |
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| author | Ying WANG Jun REN Ke SHI Bin LIN |
| author_facet | Ying WANG Jun REN Ke SHI Bin LIN |
| author_sort | Ying WANG |
| collection | DOAJ |
| description | There exist intrinsic inter-carrier interference and inter-subsymbol interference in generalized frequency division multiplexing (GFDM) systems.Under condition of time-frequency doubly selective channels, severe effects of pilot contamination would occur and lead to significant performance degradation for the pilot-based channel estimations.To this end, a channel estimation framework for GFDM systems based on deep learning was proposed, which took the low-resolution image constructed with the least squares estimated channel gains of the pilot symbols as input.Consequently, a high-resolution image about the channel time-frequency response was recovered through a deep residual network, and the channel estimation was achieved for GFDM systems.A simulation system for the proposed GFDM time-frequency doubly selective channel estimation algorithm based on deep residual network was developed, and the optimal parameters of the deep residual network were determined through an offline training process.Simulation results show that the proposed algorithm can achieve better performance near to minimum mean square error (MMSE) estimation in terms of estimation error and bit error rate (BER), and has robust Doppler frequency shift generalization capability. |
| format | Article |
| id | doaj-art-22b9a59046814a089fceab20b32a1c98 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-22b9a59046814a089fceab20b32a1c982025-01-14T07:23:02ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-10-014223324259745713Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learningYing WANGJun RENKe SHIBin LINThere exist intrinsic inter-carrier interference and inter-subsymbol interference in generalized frequency division multiplexing (GFDM) systems.Under condition of time-frequency doubly selective channels, severe effects of pilot contamination would occur and lead to significant performance degradation for the pilot-based channel estimations.To this end, a channel estimation framework for GFDM systems based on deep learning was proposed, which took the low-resolution image constructed with the least squares estimated channel gains of the pilot symbols as input.Consequently, a high-resolution image about the channel time-frequency response was recovered through a deep residual network, and the channel estimation was achieved for GFDM systems.A simulation system for the proposed GFDM time-frequency doubly selective channel estimation algorithm based on deep residual network was developed, and the optimal parameters of the deep residual network were determined through an offline training process.Simulation results show that the proposed algorithm can achieve better performance near to minimum mean square error (MMSE) estimation in terms of estimation error and bit error rate (BER), and has robust Doppler frequency shift generalization capability.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021188/generalized frequency division multiplexing (GFDM)deep learningdoubly-selective channelchannel esti-mationresidual network |
| spellingShingle | Ying WANG Jun REN Ke SHI Bin LIN Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning Tongxin xuebao generalized frequency division multiplexing (GFDM) deep learning doubly-selective channel channel esti-mation residual network |
| title | Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning |
| title_full | Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning |
| title_fullStr | Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning |
| title_full_unstemmed | Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning |
| title_short | Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning |
| title_sort | doubly selective channel estimation for generalized frequency division multiplexing systems based on deep learning |
| topic | generalized frequency division multiplexing (GFDM) deep learning doubly-selective channel channel esti-mation residual network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021188/ |
| work_keys_str_mv | AT yingwang doublyselectivechannelestimationforgeneralizedfrequencydivisionmultiplexingsystemsbasedondeeplearning AT junren doublyselectivechannelestimationforgeneralizedfrequencydivisionmultiplexingsystemsbasedondeeplearning AT keshi doublyselectivechannelestimationforgeneralizedfrequencydivisionmultiplexingsystemsbasedondeeplearning AT binlin doublyselectivechannelestimationforgeneralizedfrequencydivisionmultiplexingsystemsbasedondeeplearning |