Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation
Abstract Differential subcarrier index modulation (DSIM) system provides resource efficient information transmission without the need of channel state information and equalisation. However, maximum likelihood detector applied in DSIM possesses high complexity, which obstructs its practical applicati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-10-01
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| Series: | Electronics Letters |
| Online Access: | https://doi.org/10.1049/ell2.12619 |
| _version_ | 1811252085071544320 |
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| author | Haotian Zhang Yuan Jiang Lei Zhao |
| author_facet | Haotian Zhang Yuan Jiang Lei Zhao |
| author_sort | Haotian Zhang |
| collection | DOAJ |
| description | Abstract Differential subcarrier index modulation (DSIM) system provides resource efficient information transmission without the need of channel state information and equalisation. However, maximum likelihood detector applied in DSIM possesses high complexity, which obstructs its practical application. To address this issue, in this letter, an intelligent detector is proposed to learn the transmission patterns of DSIM to demodulate the received signals with lower complexity. In this design, the received signals are pre‐processed based on the domain knowledge at first, and then input to the deep neural network composed of multi‐layer perceptrons for further bit data estimation. Complexity analysis is provided to demonstrate the superiority of the proposed design, and simulation results validate that the intelligent detector can achieve similar average bit error rate with higher robustness compared with the benchmark system. |
| first_indexed | 2024-04-12T16:29:33Z |
| format | Article |
| id | doaj.art-bf0cc7d0ef2d4bf0a82734ce639c1380 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-04-12T16:29:33Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-bf0cc7d0ef2d4bf0a82734ce639c13802022-12-22T03:25:14ZengWileyElectronics Letters0013-51941350-911X2022-10-01582285385510.1049/ell2.12619Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulationHaotian Zhang0Yuan Jiang1Lei Zhao2School of Electronics and Communication Engineering Sun Yat‐sen University Shenzhen ChinaSchool of Electronics and Communication Engineering Sun Yat‐sen University Shenzhen ChinaSchool of Electronics and Communication Engineering Sun Yat‐sen University Shenzhen ChinaAbstract Differential subcarrier index modulation (DSIM) system provides resource efficient information transmission without the need of channel state information and equalisation. However, maximum likelihood detector applied in DSIM possesses high complexity, which obstructs its practical application. To address this issue, in this letter, an intelligent detector is proposed to learn the transmission patterns of DSIM to demodulate the received signals with lower complexity. In this design, the received signals are pre‐processed based on the domain knowledge at first, and then input to the deep neural network composed of multi‐layer perceptrons for further bit data estimation. Complexity analysis is provided to demonstrate the superiority of the proposed design, and simulation results validate that the intelligent detector can achieve similar average bit error rate with higher robustness compared with the benchmark system.https://doi.org/10.1049/ell2.12619 |
| spellingShingle | Haotian Zhang Yuan Jiang Lei Zhao Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation Electronics Letters |
| title | Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation |
| title_full | Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation |
| title_fullStr | Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation |
| title_full_unstemmed | Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation |
| title_short | Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation |
| title_sort | low complexity and robust mlp mixer aided detector design based on deep learning for differential subcarrier index modulation |
| url | https://doi.org/10.1049/ell2.12619 |
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