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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.11327 |
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| _version_ | 1866914166913105920 |
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| author | Xiang, Cheng Huang, Yu Wen, Miaowen Tan, Weiqiang Chae, Chan-Byoung |
| author_facet | Xiang, Cheng Huang, Yu Wen, Miaowen Tan, Weiqiang Chae, Chan-Byoung |
| contents | In molecular communications (MC), inter-symbol interference (ISI) and noise are key factors that degrade communication reliability. Although time-domain equalization can effectively mitigate these effects, it often entails high computational complexity concerning the channel memory. In contrast, frequency-domain equalization (FDE) offers greater computational efficiency but typically requires prior knowledge of the channel model. To address this limitation, this letter proposes FDE techniques based on long short-term memory (LSTM) neural networks, enabling temporal correlation modeling in MC channels to improve ISI and noise suppression. To eliminate the reliance on prior channel information in conventional FDE methods, a supervised training strategy is employed for channel-adaptive equalization. Simulation results demonstrate that the proposed LSTM-FDE significantly reduces the bit error rate compared to traditional FDE and feedforward neural network-based equalizers. This performance gain is attributed to the LSTM's temporal modeling capabilities, which enhance noise suppression and accelerate model convergence, while maintaining comparable computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Equalize: Data-Driven Frequency-Domain Signal Recovery in Molecular Communications Xiang, Cheng Huang, Yu Wen, Miaowen Tan, Weiqiang Chae, Chan-Byoung Subcellular Processes Information Theory In molecular communications (MC), inter-symbol interference (ISI) and noise are key factors that degrade communication reliability. Although time-domain equalization can effectively mitigate these effects, it often entails high computational complexity concerning the channel memory. In contrast, frequency-domain equalization (FDE) offers greater computational efficiency but typically requires prior knowledge of the channel model. To address this limitation, this letter proposes FDE techniques based on long short-term memory (LSTM) neural networks, enabling temporal correlation modeling in MC channels to improve ISI and noise suppression. To eliminate the reliance on prior channel information in conventional FDE methods, a supervised training strategy is employed for channel-adaptive equalization. Simulation results demonstrate that the proposed LSTM-FDE significantly reduces the bit error rate compared to traditional FDE and feedforward neural network-based equalizers. This performance gain is attributed to the LSTM's temporal modeling capabilities, which enhance noise suppression and accelerate model convergence, while maintaining comparable computational efficiency. |
| title | Learning to Equalize: Data-Driven Frequency-Domain Signal Recovery in Molecular Communications |
| topic | Subcellular Processes Information Theory |
| url | https://arxiv.org/abs/2509.11327 |