使用机器学习调整自由空间光学通道的参数
Applied optics
|June 10, 2024
概括
人工智能精确模拟自由空间光学 (FSO) 数据传输. 机器学习模型以高准确度预测FSO道性能,证实了AI方法论的使用.
科学领域:
- 光学通信是指光学通信.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自由空间光学 (FSO) 技术提供高速数据传输,但需要准确的性能预测.
- 多参数模拟对于了解各种条件下的FSO系统行为至关重要.
研究的目的:
- 应用人工智能 (AI) 和机器学习 (ML) 来模拟和预测FSO系统中的数据传输性能.
- 评估不同的AI回归模型在估计FSO通道的最大质量因子 (MaxQFactor) 的有效性.
主要方法:
- 使用的Optisystem软件用于FSO数据传输的多参数数值模拟.
- 训练了各种人工智能模型,包括决策树回归 (DTR),随机森林回归 (RFR),梯度提升回归器 (GBR),直方图梯度提升回归器 (HGBR) 和AdaBoost +决策树回归 (ADDTR).
- 使用确定系数 (R2) 评估模型性能,考虑距离,衰减,放大器增益,光束分歧和接收器直径等参数.
主要成果:
- 对于第一个模拟集 (距离,衰减,放大器增益),DTR和RFR模型实现了优异的预测准确性 (R2>95.00%).
- 在第二个模拟组中,DTR和RFR模型也显示出出色的结果 (R2>94.00%),包括光束分歧和接收器直径.
- 图形比较证实了人工智能方法在预测FSO通道输出值方面的高效性.
结论:
- 人工智能和机器学习方法为模拟和预测FSO数据传输性能提供了高度有效和准确的方法.
- 训练有素的AI模型,特别是DTR和RFR,可以可靠地估计FSO系统中的关键性能指标,如MaxQFactor.
- 这种人工智能驱动的模拟方法提高了FSO通信系统的理解和优化.
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