Low-Complexity Data-Driven Communication Neural Receivers
Communication receivers based on deep learning have become a research hotspot in recent years. However, the excessive computational complexity and storage complexity prevent it from being deployed on communication hardware with limited resources. In order to reduce the computational complexity and r...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819393/ |
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| author | Qingle Wu Yuanhui Liang Benjamin K. Ng Chan-Tong Lam and Yan Ma |
| author_facet | Qingle Wu Yuanhui Liang Benjamin K. Ng Chan-Tong Lam and Yan Ma |
| author_sort | Qingle Wu |
| collection | DOAJ |
| description | Communication receivers based on deep learning have become a research hotspot in recent years. However, the excessive computational complexity and storage complexity prevent it from being deployed on communication hardware with limited resources. In order to reduce the computational complexity and required storage resources of communication neural receivers based on deep learning, we propose to use candecomp parafac (CP), Tucker, tensor-train (TT) and tensor-ring (TR) decomposition respectively to compress the data-driven deep learning based communication neural receiver. Through compression, the storage resources required by the communication neural receiver are reduced by about half with bit error rate (BER) performance degradation of only 0.75dB to 1.3dB. |
| format | Article |
| id | doaj-art-01d4987a26a2423ab598813c67b97e85 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-01d4987a26a2423ab598813c67b97e852025-01-21T00:02:21ZengIEEEIEEE Access2169-35362025-01-01139325933410.1109/ACCESS.2024.352457110819393Low-Complexity Data-Driven Communication Neural ReceiversQingle Wu0https://orcid.org/0000-0002-3295-6248Yuanhui Liang1https://orcid.org/0009-0003-1247-2451Benjamin K. Ng2https://orcid.org/0000-0001-5901-5694Chan-Tong Lam3https://orcid.org/0000-0002-8022-7744and Yan Ma4https://orcid.org/0000-0001-8065-591XSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao, ChinaBUPT Network Information Center, Beijing University of Posts and Telecommunications, Beijing, ChinaCommunication receivers based on deep learning have become a research hotspot in recent years. However, the excessive computational complexity and storage complexity prevent it from being deployed on communication hardware with limited resources. In order to reduce the computational complexity and required storage resources of communication neural receivers based on deep learning, we propose to use candecomp parafac (CP), Tucker, tensor-train (TT) and tensor-ring (TR) decomposition respectively to compress the data-driven deep learning based communication neural receiver. Through compression, the storage resources required by the communication neural receiver are reduced by about half with bit error rate (BER) performance degradation of only 0.75dB to 1.3dB.https://ieeexplore.ieee.org/document/10819393/Tensor-train decompositiontensor-ring decompositioncomputational complexitycommunication neural receiverstorage complexity |
| spellingShingle | Qingle Wu Yuanhui Liang Benjamin K. Ng Chan-Tong Lam and Yan Ma Low-Complexity Data-Driven Communication Neural Receivers IEEE Access Tensor-train decomposition tensor-ring decomposition computational complexity communication neural receiver storage complexity |
| title | Low-Complexity Data-Driven Communication Neural Receivers |
| title_full | Low-Complexity Data-Driven Communication Neural Receivers |
| title_fullStr | Low-Complexity Data-Driven Communication Neural Receivers |
| title_full_unstemmed | Low-Complexity Data-Driven Communication Neural Receivers |
| title_short | Low-Complexity Data-Driven Communication Neural Receivers |
| title_sort | low complexity data driven communication neural receivers |
| topic | Tensor-train decomposition tensor-ring decomposition computational complexity communication neural receiver storage complexity |
| url | https://ieeexplore.ieee.org/document/10819393/ |
| work_keys_str_mv | AT qinglewu lowcomplexitydatadrivencommunicationneuralreceivers AT yuanhuiliang lowcomplexitydatadrivencommunicationneuralreceivers AT benjaminkng lowcomplexitydatadrivencommunicationneuralreceivers AT chantonglam lowcomplexitydatadrivencommunicationneuralreceivers AT andyanma lowcomplexitydatadrivencommunicationneuralreceivers |