Deep Learning With Persistent Homology for Orbital Angular Momentum (OAM) Decoding
Deep Learning With Persistent Homology for Orbital Angular Momentum (OAM) Decoding
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DOI：
10.1109/lcomm.2019.2954311
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Soheil Rostami;W. Saad;C. Hong
中科院分区：
文献类型：
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作者：
Soheil Rostami;W. Saad;C. Hong
轨道角动量（OAM）编码是近年来发展起来的一种提高自由空间光通信信道容量的有效方法。在这封信中，OAM为基础的解码制定为监督分类问题。为了在存在严重大气湍流的情况下保持较低的错误率，提出了一种结合持久同源性和卷积神经网络（CNN）的有效机器学习工具来解码OAM模式的新方法。提出了一种具有可学习参数的高斯核，以便将持久同源性连接到CNN，使系统能够提取和区分OAM模式的鲁棒和独特的拓扑特征。仿真结果表明，所提出的方法实现了高达20%的收益，在分类准确率比国家的最先进的方法的基础上，只有CNN。这些结果基本上表明，几何和拓扑特征在OAM模式分类问题中起着关键作用。
Orbital angular momentum (OAM)-encoding has recently emerged as an effective approach for increasing the channel capacity of free-space optical communications. In this letter, OAM-based decoding is formulated as a supervised classification problem. To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology and convolutional neural networks (CNNs) is proposed to decode the OAM modes. A Gaussian kernel with learnable parameters is proposed in order to connect persistent homology to CNN, allowing the system to extract and distinguish robust and unique topological features for the OAM modes. Simulation results show that the proposed approach achieves up to 20% gains in classification accuracy rate over state-of-the-art of method based on only CNNs. These results essentially show that geometric and topological features play a pivotal role in the OAM mode classification problem.