Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing
Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor pe...
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| Format: | Article |
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China InfoCom Media Group
2021-09-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00227/ |
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| _version_ | 1841531147597643776 |
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| author | Yiyang HU Lina QI |
| author_facet | Yiyang HU Lina QI |
| author_sort | Yiyang HU |
| collection | DOAJ |
| description | Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity. |
| format | Article |
| id | doaj-art-34c8582a8b754721b32c5c9e10a1defb |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2021-09-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-34c8582a8b754721b32c5c9e10a1defb2025-01-15T02:53:18ZzhoChina InfoCom Media Group物联网学报2096-37502021-09-015788559648204Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensingYiyang HULina QIMassive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00227/internet of thingsmassive MIMOcompressed sensingchannel estimationsparsity adaptivedifferential CIR |
| spellingShingle | Yiyang HU Lina QI Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing 物联网学报 internet of things massive MIMO compressed sensing channel estimation sparsity adaptive differential CIR |
| title | Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing |
| title_full | Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing |
| title_fullStr | Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing |
| title_full_unstemmed | Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing |
| title_short | Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing |
| title_sort | channel estimation method of massive mimo ofdm system based on adaptive compressed sensing |
| topic | internet of things massive MIMO compressed sensing channel estimation sparsity adaptive differential CIR |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00227/ |
| work_keys_str_mv | AT yiyanghu channelestimationmethodofmassivemimoofdmsystembasedonadaptivecompressedsensing AT linaqi channelestimationmethodofmassivemimoofdmsystembasedonadaptivecompressedsensing |