Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder
Channel estimation is one of the most important aspects of wireless communication. Especially, in sparse code multiple access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8667100/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839617323166597120 |
|---|---|
| author | Quan Yuan Dezhi Li Zhenyong Wang Chang Liu Ci He |
| author_facet | Quan Yuan Dezhi Li Zhenyong Wang Chang Liu Ci He |
| author_sort | Quan Yuan |
| collection | DOAJ |
| description | Channel estimation is one of the most important aspects of wireless communication. Especially, in sparse code multiple access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time–frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things (IoT) when time–frequency resources are restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in an SCMA system based on complex-valued sparse autoencoder which is effective to learn features of the wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In this paper, the encoder part is used to realize the pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of the linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates’ performance of the SCMA receiver in this paper is very close to that of the perfect channel state information (CSI). |
| format | Article |
| id | doaj-art-fbe5140ccf6b4575b898c315f16c8e7b |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fbe5140ccf6b4575b898c315f16c8e7b2025-07-24T23:01:04ZengIEEEIEEE Access2169-35362025-01-011312355912356910.1109/ACCESS.2019.29049908667100Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse AutoencoderQuan Yuan0https://orcid.org/0000-0003-2152-4375Dezhi Li1Zhenyong Wang2https://orcid.org/0000-0001-8236-7073Chang Liu3Ci He4School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaScience and Technology on Communication Networks Laboratory, Shijiazhuang, ChinaChannel estimation is one of the most important aspects of wireless communication. Especially, in sparse code multiple access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time–frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things (IoT) when time–frequency resources are restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in an SCMA system based on complex-valued sparse autoencoder which is effective to learn features of the wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In this paper, the encoder part is used to realize the pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of the linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates’ performance of the SCMA receiver in this paper is very close to that of the perfect channel state information (CSI).https://ieeexplore.ieee.org/document/8667100/Sparse code multiple accesschannel estimationsparse pilot structureInternet of Thingsmassive connectioncomplex-valued sparse autoencoder |
| spellingShingle | Quan Yuan Dezhi Li Zhenyong Wang Chang Liu Ci He Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder IEEE Access Sparse code multiple access channel estimation sparse pilot structure Internet of Things massive connection complex-valued sparse autoencoder |
| title | Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder |
| title_full | Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder |
| title_fullStr | Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder |
| title_full_unstemmed | Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder |
| title_short | Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder |
| title_sort | channel estimation and pilot design for uplink sparse code multiple access system based on complex valued sparse autoencoder |
| topic | Sparse code multiple access channel estimation sparse pilot structure Internet of Things massive connection complex-valued sparse autoencoder |
| url | https://ieeexplore.ieee.org/document/8667100/ |
| work_keys_str_mv | AT quanyuan channelestimationandpilotdesignforuplinksparsecodemultipleaccesssystembasedoncomplexvaluedsparseautoencoder AT dezhili channelestimationandpilotdesignforuplinksparsecodemultipleaccesssystembasedoncomplexvaluedsparseautoencoder AT zhenyongwang channelestimationandpilotdesignforuplinksparsecodemultipleaccesssystembasedoncomplexvaluedsparseautoencoder AT changliu channelestimationandpilotdesignforuplinksparsecodemultipleaccesssystembasedoncomplexvaluedsparseautoencoder AT cihe channelestimationandpilotdesignforuplinksparsecodemultipleaccesssystembasedoncomplexvaluedsparseautoencoder |