Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems

• URL: http://arxiv.org/abs/2508.13783v1
• Date: Tue, 19 Aug 2025 12:32:13 GMT
• Title: Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems
• Authors: Eike-Manuel Edelmann, Alexander von Bank, Laurent Schmalen,
• Abstract summary: We propose a reinforcement learning-based algorithm to optimize spiking neural networks (SNNs)<n>applied to an SNN-based equalizer and demapper in an IM/DD system, the method improves performance while reducing computational load and network size.
• Score: 49.34817254755008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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