When a huge number of couplers are required in a large-scale beam forming network (BFN), an efficient coupler automatic design method is in demand. In this letter, a new data-driven-based modular neural network (DD-MNN) is proposed to address this problem. The framework consists of five submodules containing artificial neural networks (ANNs). First, training samples are divided into satisfying and unsatisfying couplers. Second, neural networks in submodules are trained to learn the deep relations between electrical and geometrical parameters from satisfying couplers. Third, the learned knowledge from satisfying samples is applied to unsatisfying ones to provide optimization suggestions. Finally, data are returned back to drive the fine-tuning of ANNs. An experiment using 75 four-port couplers is designed, showing that the method can output the optimized structure with over 95% improvement. Finally, the framework is implemented to design ten couplers given $S_{31}/S_{41}$ , showing that the proposed method could perform an efficient coupler automatic design.

中文翻译：

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LoRa 网络的能效分析


当大规模波束形成网络（BFN）中需要大量耦合器时，需要一种高效的耦合器自动设计方法。在这封信中，提出了一种新的基于数据驱动的模块化神经网络（DD-MNN）来解决这个问题。该框架由五个包含人工神经网络 (ANN) 的子模块组成。首先，将训练样本分为满意耦合器和不满意耦合器。其次，子模块中的神经网络经过训练，可以从令人满意的耦合器中学习电气参数和几何参数之间的深层关系。第三，从满意样本中学到的知识应用于不满意样本，以提供优化建议。最后，数据返回以驱动人工神经网络的微调。设计了使用75个四端口耦合器的实验，表明该方法可以输出优化后的结构，改进率超过95%。最后，在给定 $S_{31}/S_{41}$ 的情况下，实现该框架来设计十个耦合器，表明所提出的方法可以执行有效的耦合器自动设计。