{"ID":2880663,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13783","arxiv_id":"2508.13783","title":"Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems","abstract":"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.","short_abstract":"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.","url_abs":"https://arxiv.org/abs/2508.13783","url_pdf":"https://arxiv.org/pdf/2508.13783v1","authors":"[\"Eike-Manuel Edelmann\",\"Alexander von Bank\",\"Laurent Schmalen\"]","published":"2025-08-19T12:32:13Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"eess.SP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}