Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems
Massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems play a vital role in high-mobility scenarios like vehicle-to-everything and high-speed railways, offering strong resilience against Doppler effects and mitigating channel degradation. However, accurate chann...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025146/ |
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| _version_ | 1849227133883252736 |
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| author | WANG Han WEN Fangqing LI Xingwang WANG Xianpeng |
| author_facet | WANG Han WEN Fangqing LI Xingwang WANG Xianpeng |
| author_sort | WANG Han |
| collection | DOAJ |
| description | Massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems play a vital role in high-mobility scenarios like vehicle-to-everything and high-speed railways, offering strong resilience against Doppler effects and mitigating channel degradation. However, accurate channel estimation remains a challenge, especially in dynamic environments where conventional methods like orthogonal matching pursuit (OMP) struggle. A two-dimensional regularized orthogonal matching pursuit (2D-ROMP) algorithm tailored was introduced for channel estimation in massive MIMO OTFS systems. Unlike traditional OMP-based methods, in which delay and Doppler shifts were estimated independently, the proposed algorithm combined these two dimensions into a unified delay-Doppler (DD) domain, effectively leveraging the multi-dimensional sparsity of OTFS channels. By employing regularization, the most relevant DD indices were first identified, , followed by refinement in the antenna angle domain to leverage burst sparsity. Compared to conventional OMP and three-dimensional structured OMP (3D-SOMP) methods, the proposed algorithm was demonstrated to achieve improved channel estimation accuracy and computational efficiency. Simulation results demonstrate that it outperforms existing approaches with reduced pilot overhead and enhanced robustness in dynamic environments. |
| format | Article |
| id | doaj-art-231be00a3b7a4205800fca29896e4b76 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-231be00a3b7a4205800fca29896e4b762025-08-23T19:00:10ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-01-01111123345569Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systemsWANG HanWEN FangqingLI XingwangWANG XianpengMassive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems play a vital role in high-mobility scenarios like vehicle-to-everything and high-speed railways, offering strong resilience against Doppler effects and mitigating channel degradation. However, accurate channel estimation remains a challenge, especially in dynamic environments where conventional methods like orthogonal matching pursuit (OMP) struggle. A two-dimensional regularized orthogonal matching pursuit (2D-ROMP) algorithm tailored was introduced for channel estimation in massive MIMO OTFS systems. Unlike traditional OMP-based methods, in which delay and Doppler shifts were estimated independently, the proposed algorithm combined these two dimensions into a unified delay-Doppler (DD) domain, effectively leveraging the multi-dimensional sparsity of OTFS channels. By employing regularization, the most relevant DD indices were first identified, , followed by refinement in the antenna angle domain to leverage burst sparsity. Compared to conventional OMP and three-dimensional structured OMP (3D-SOMP) methods, the proposed algorithm was demonstrated to achieve improved channel estimation accuracy and computational efficiency. Simulation results demonstrate that it outperforms existing approaches with reduced pilot overhead and enhanced robustness in dynamic environments.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025146/two-dimensionalmassive MIMOOTFSchannel estimationsparse recovery |
| spellingShingle | WANG Han WEN Fangqing LI Xingwang WANG Xianpeng Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems Tongxin xuebao two-dimensional massive MIMO OTFS channel estimation sparse recovery |
| title | Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems |
| title_full | Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems |
| title_fullStr | Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems |
| title_full_unstemmed | Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems |
| title_short | Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems |
| title_sort | two dimensional sparse recovery method for channel estimation in massive mimo otfs systems |
| topic | two-dimensional massive MIMO OTFS channel estimation sparse recovery |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025146/ |
| work_keys_str_mv | AT wanghan twodimensionalsparserecoverymethodforchannelestimationinmassivemimootfssystems AT wenfangqing twodimensionalsparserecoverymethodforchannelestimationinmassivemimootfssystems AT lixingwang twodimensionalsparserecoverymethodforchannelestimationinmassivemimootfssystems AT wangxianpeng twodimensionalsparserecoverymethodforchannelestimationinmassivemimootfssystems |