THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network
To mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems, a deep learning-based FPN-OAMP-SRLG algorithm was proposed. A feature extraction network SRLG was constructed by developing a deep residual block (BSRB) with coor...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025093 |
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| _version_ | 1850226169859801088 |
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| author | YU Shujuan WEI Yuyao CAI Lianglong LU Hongyu ZHANG Yun ZHAO Shengmei |
| author_facet | YU Shujuan WEI Yuyao CAI Lianglong LU Hongyu ZHANG Yun ZHAO Shengmei |
| author_sort | YU Shujuan |
| collection | DOAJ |
| description | To mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems, a deep learning-based FPN-OAMP-SRLG algorithm was proposed. A feature extraction network SRLG was constructed by developing a deep residual block (BSRB) with coordinate attention and partial channel shift, along with a gated linear self-attention module (SARG). The channel estimation problem was formulated as an image restoration task through integration with the FPN-OAMP framework. The algorithm utilized pilot information, estimated via the least squares method, as input features and recovered channel state information through iterative linear and nonlinear estimators. Simulation results demonstrate that the proposed algorithm achieves high-precision THz channel estimation, exhibiting fast convergence and robust performance. |
| format | Article |
| id | doaj-art-64045072e6db47ec986ca94de5de4221 |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-64045072e6db47ec986ca94de5de42212025-08-20T02:05:09ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-05-01467790108590198THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point networkYU ShujuanWEI YuyaoCAI LianglongLU HongyuZHANG YunZHAO ShengmeiTo mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems, a deep learning-based FPN-OAMP-SRLG algorithm was proposed. A feature extraction network SRLG was constructed by developing a deep residual block (BSRB) with coordinate attention and partial channel shift, along with a gated linear self-attention module (SARG). The channel estimation problem was formulated as an image restoration task through integration with the FPN-OAMP framework. The algorithm utilized pilot information, estimated via the least squares method, as input features and recovered channel state information through iterative linear and nonlinear estimators. Simulation results demonstrate that the proposed algorithm achieves high-precision THz channel estimation, exhibiting fast convergence and robust performance.http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025093channel estimationTHz ultra-massive MIMO systemdeep residual blockattention mechanism |
| spellingShingle | YU Shujuan WEI Yuyao CAI Lianglong LU Hongyu ZHANG Yun ZHAO Shengmei THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network Tongxin xuebao channel estimation THz ultra-massive MIMO system deep residual block attention mechanism |
| title | THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network |
| title_full | THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network |
| title_fullStr | THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network |
| title_full_unstemmed | THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network |
| title_short | THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network |
| title_sort | thz um mimo system channel estimation algorithm based on deep residual block fixed point network |
| topic | channel estimation THz ultra-massive MIMO system deep residual block attention mechanism |
| url | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025093 |
| work_keys_str_mv | AT yushujuan thzummimosystemchannelestimationalgorithmbasedondeepresidualblockfixedpointnetwork AT weiyuyao thzummimosystemchannelestimationalgorithmbasedondeepresidualblockfixedpointnetwork AT cailianglong thzummimosystemchannelestimationalgorithmbasedondeepresidualblockfixedpointnetwork AT luhongyu thzummimosystemchannelestimationalgorithmbasedondeepresidualblockfixedpointnetwork AT zhangyun thzummimosystemchannelestimationalgorithmbasedondeepresidualblockfixedpointnetwork AT zhaoshengmei thzummimosystemchannelestimationalgorithmbasedondeepresidualblockfixedpointnetwork |