Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design
Sparse code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In SCDMA, transmitted symbols from multiple users are coded by their own sparse signature sequences, and a base station attempts to detect those symbols using the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9373341/ |
| _version_ | 1819276857417138176 |
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| author | Satoshi Takabe Yuki Yamauchi Tadashi Wadayama |
| author_facet | Satoshi Takabe Yuki Yamauchi Tadashi Wadayama |
| author_sort | Satoshi Takabe |
| collection | DOAJ |
| description | Sparse code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In SCDMA, transmitted symbols from multiple users are coded by their own sparse signature sequences, and a base station attempts to detect those symbols using the signature sequences. In this paper, we present a new deep-unfolded multiuser detector called a complex sparse trainable projected gradient (C-STPG) detector for SCDMA systems. Deep unfolding is a deep learning method that tunes trainable parameters in iterative algorithms using supervised data and deep learning techniques. The proposed detector provides a much superior detection performance over that of the LMMSE detector. Other advantages of the proposed detector include a low computational complexity in execution and a low training cost owing to the small number of trainable parameters. In addition, we propose a novel joint learning strategy called gradual sparsification for designing sparse signature sequences based on deep unfolding. This method is computationally efficient in optimizing a set of sparse signature sequences. Numerical results show that the gradual sparsification successfully yields sparse signature sequences with a smaller symbol error rate than those of randomly designed sparse signature sequences. |
| first_indexed | 2024-12-23T23:46:53Z |
| format | Article |
| id | doaj.art-143651b8898f4f1493ba91ac87c450c9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:46:53Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-143651b8898f4f1493ba91ac87c450c92022-12-21T17:25:29ZengIEEEIEEE Access2169-35362021-01-019400274003810.1109/ACCESS.2021.30645589373341Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature DesignSatoshi Takabe0https://orcid.org/0000-0002-5755-2231Yuki Yamauchi1Tadashi Wadayama2https://orcid.org/0000-0003-4391-4294Nagoya Institute of Technology, Nagoya, JapanNagoya Institute of Technology, Nagoya, JapanNagoya Institute of Technology, Nagoya, JapanSparse code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In SCDMA, transmitted symbols from multiple users are coded by their own sparse signature sequences, and a base station attempts to detect those symbols using the signature sequences. In this paper, we present a new deep-unfolded multiuser detector called a complex sparse trainable projected gradient (C-STPG) detector for SCDMA systems. Deep unfolding is a deep learning method that tunes trainable parameters in iterative algorithms using supervised data and deep learning techniques. The proposed detector provides a much superior detection performance over that of the LMMSE detector. Other advantages of the proposed detector include a low computational complexity in execution and a low training cost owing to the small number of trainable parameters. In addition, we propose a novel joint learning strategy called gradual sparsification for designing sparse signature sequences based on deep unfolding. This method is computationally efficient in optimizing a set of sparse signature sequences. Numerical results show that the gradual sparsification successfully yields sparse signature sequences with a smaller symbol error rate than those of randomly designed sparse signature sequences.https://ieeexplore.ieee.org/document/9373341/SCDMAdeep learningdeep unfoldingmultiuser detectorsignature design |
| spellingShingle | Satoshi Takabe Yuki Yamauchi Tadashi Wadayama Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design IEEE Access SCDMA deep learning deep unfolding multiuser detector signature design |
| title | Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design |
| title_full | Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design |
| title_fullStr | Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design |
| title_full_unstemmed | Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design |
| title_short | Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design |
| title_sort | deep unfolded sparse cdma multiuser detector and sparse signature design |
| topic | SCDMA deep learning deep unfolding multiuser detector signature design |
| url | https://ieeexplore.ieee.org/document/9373341/ |
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