Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916497149919232 |
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| author | Cheng, Yukun Chen, Wei Hou, Tianwei Li, Geoffrey Ye Ai, Bo |
| author_facet | Cheng, Yukun Chen, Wei Hou, Tianwei Li, Geoffrey Ye Ai, Bo |
| contents | Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel conditions while it is costly to store multiple AI models or parameter sets. In this article, we propose an auto-encoder (AE) based rate-compatible linear block codes (RC-LBCs). The coding process associated with AI or non-AI decoders and multiple puncturing patterns is optimized in a data-driven manner. The superior performance of the proposed AI-based RC-LBC is demonstrated through our numerical experiments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_18153 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach Cheng, Yukun Chen, Wei Hou, Tianwei Li, Geoffrey Ye Ai, Bo Signal Processing Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel conditions while it is costly to store multiple AI models or parameter sets. In this article, we propose an auto-encoder (AE) based rate-compatible linear block codes (RC-LBCs). The coding process associated with AI or non-AI decoders and multiple puncturing patterns is optimized in a data-driven manner. The superior performance of the proposed AI-based RC-LBC is demonstrated through our numerical experiments. |
| title | Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.18153 |