Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques
This paper considers channel estimation problem in massive MIMO partially centralized cloud-RAN. By noting that the user activities in massive MIMO partially centralized cloud-RAN are sparse, the channel estimation issue is solved by use of compressed data method to minimize the huge pilot overhead....
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| Format: | Article |
| Language: | English |
| Published: |
FRUCT
2020-04-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/acm26/files/Muk.pdf |
| _version_ | 1818199065071976448 |
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| author | Emmanuel Mukubwa Oludare Sokoya |
| author_facet | Emmanuel Mukubwa Oludare Sokoya |
| author_sort | Emmanuel Mukubwa |
| collection | DOAJ |
| description | This paper considers channel estimation problem in massive MIMO partially centralized cloud-RAN. By noting that the user activities in massive MIMO partially centralized cloud-RAN are sparse, the channel estimation issue is solved by use of compressed data method to minimize the huge pilot overhead. Comparison and analysis of improved MMSE, via-Q and compressed data methods are done for massive MIMO partially centralized cloud-RAN. The achievable spectral efficiency (SE) and normalized mean square error (NMSE) were investigated. The RNA-based estimator gave the best performance for spectral efficiency than the MR for the multicell massive MIMO partially centralized cloud-RAN system. The performance is also evaluated for normalized mean square error for the three estimators with the RNA-MMSE giving the lowest normalized mean square error. The performance between the compressed CSI and the via-Q method show that the two methods are comparable, and this vindicates compressed data as a method to be utilized in channel state information covariance matrix estimation since it compresses the massive MIMO channel information hence mitigating the fronthaul finite capacity problem. |
| first_indexed | 2024-12-12T02:15:50Z |
| format | Article |
| id | doaj.art-4d5015f4dec847f39e913b2dc9d912cd |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-12T02:15:50Z |
| publishDate | 2020-04-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-4d5015f4dec847f39e913b2dc9d912cd2022-12-22T00:41:48ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372020-04-0126260461010.5281/zenodo.4007436Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data TechniquesEmmanuel Mukubwa0Oludare Sokoya1Durban university of technology, South AfricaDurban university of Technology, South AfricaThis paper considers channel estimation problem in massive MIMO partially centralized cloud-RAN. By noting that the user activities in massive MIMO partially centralized cloud-RAN are sparse, the channel estimation issue is solved by use of compressed data method to minimize the huge pilot overhead. Comparison and analysis of improved MMSE, via-Q and compressed data methods are done for massive MIMO partially centralized cloud-RAN. The achievable spectral efficiency (SE) and normalized mean square error (NMSE) were investigated. The RNA-based estimator gave the best performance for spectral efficiency than the MR for the multicell massive MIMO partially centralized cloud-RAN system. The performance is also evaluated for normalized mean square error for the three estimators with the RNA-MMSE giving the lowest normalized mean square error. The performance between the compressed CSI and the via-Q method show that the two methods are comparable, and this vindicates compressed data as a method to be utilized in channel state information covariance matrix estimation since it compresses the massive MIMO channel information hence mitigating the fronthaul finite capacity problem.https://www.fruct.org/publications/acm26/files/Muk.pdfc-ranmassive mimocompressed datavia-q method. |
| spellingShingle | Emmanuel Mukubwa Oludare Sokoya Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques Proceedings of the XXth Conference of Open Innovations Association FRUCT c-ran massive mimo compressed data via-q method. |
| title | Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques |
| title_full | Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques |
| title_fullStr | Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques |
| title_full_unstemmed | Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques |
| title_short | Analysis of Channel Estimation Performance in MPC-RAN: Improved MMSE and Compressed Data Techniques |
| title_sort | analysis of channel estimation performance in mpc ran improved mmse and compressed data techniques |
| topic | c-ran massive mimo compressed data via-q method. |
| url | https://www.fruct.org/publications/acm26/files/Muk.pdf |
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