概括
本研究比较了光学无线通信 (OWC) 中的三个端到端 (E2E) 学习方法. 综合自编码器 (CAE) 模型提供了优越的带宽分配,而基于自编码器的几何成型 (AEGS) 模型在接收光学功率增益方面表现出色,特别是在流状态下.
科学领域:
- 光学无线通信 (OWC) 是一种无线通信.
- 6G 原生智能系统
- 终端到终端 (E2E) 学习
背景情况:
- E2E学习是6G智能系统中最佳通信的有希望的方法.
- 缺乏比较分析阻碍了对E2E技术的适用性和有效性的理解.
- 光学无线通信 (OWC) 系统面临带宽限制和环境动荡的挑战.
研究的目的:
- 综合比较三个先进的E2E方法:基于自动编码器的几何造型 (AEGS),综合自动编码器 (CAE) 和波式自动等级 (WWAE).
- 在一个真正的带宽有限的OWC系统中评估它们的性能.
- 引入一种新的基于注意力的综合噪声联合通道估计器 (ACNJCE) 作为通用通道模型.
主要方法:
- 在真正的OWC系统中实施和比较AEGS,CAE和WWAE模型.
- 使用一个新的ACNJCE用于可适应的通道建模.
- 在无载波振幅和相调制 (CAP) 和比特功率负载离散多音调制 (DMT) 下进行测试,波率可变 (2-3 GBaud).
主要成果:
- CAE证明了自主带宽分配和69%更高的动态调整范围,而不是NN等同的CAP.
- 在流下,AEGS显示了显著的接收光功率 (ROP) 增益和卓越,稳定的性能.
- 使用DMT的WWAE实现了最高的数据速率 (近1GHz带宽),其BER率为NN等级的25.2%.
结论:
- 这项研究提供了在真实OWC系统中首次全面评估主要的E2E算法.
- CAE和AEGS分别在带宽分配和ROP收益方面提供了明显的优势.
- 在光谱受约束的OWC系统中,WWAE与DMT对于高数据速率是有效的,而AEGS显示出对流的弹性.
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