Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems
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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Reinforcement LearningSimilar Papers 제목 키워드 기반
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