Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915451325382656 |
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| author | Edelmann, Eike-Manuel von Bank, Alexander Schmalen, Laurent |
| author_facet | Edelmann, Eike-Manuel von Bank, Alexander Schmalen, Laurent |
| contents | Neural encoding parameters for spiking neural networks (SNNs) are typically set heuristically. We propose a reinforcement learning-based algorithm to optimize them. Applied to an SNN-based equalizer and demapper in an IM/DD system, the method improves performance while reducing computational load and network size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13783 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems Edelmann, Eike-Manuel von Bank, Alexander Schmalen, Laurent Neural and Evolutionary Computing Signal Processing Neural encoding parameters for spiking neural networks (SNNs) are typically set heuristically. We propose a reinforcement learning-based algorithm to optimize them. Applied to an SNN-based equalizer and demapper in an IM/DD system, the method improves performance while reducing computational load and network size. |
| title | Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems |
| topic | Neural and Evolutionary Computing Signal Processing |
| url | https://arxiv.org/abs/2508.13783 |