论文标题
深卷卷卷学习辅助检测器，用于通用频施加索引调制
Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulation
论文作者
论文摘要
在本文中，提出了一种深卷积神经网络的符号检测和解调，用于使用索引调制（GFDM-IM）方案进行广义频施工多路复用，以提高系统的误差性能。提出的方法首先使用零孔（ZF）检测器预处理信号，然后使用由卷积神经网络（CNN）组成的神经网络，然后是完全连接的神经网络（FCNN）。 FCNN部分仅使用两个完全连接的层，可以对其进行调整以在复杂性和位错误率（BER）性能之间进行权衡。这种两阶段的方法阻止了在鞍点上陷入神经网络的困扰，并使IM可以独立地进行处理。已经证明，与ZF检测器相比，提出的深卷卷卷神经网络的检测和解调方案具有更高的BER性能，并具有合理的复杂性增加。我们得出的结论是，在深度学习的帮助下，非正交波形与IM方案相结合是一种有希望的物理层（PHY）方案
In this paper, a deep convolutional neural network-based symbol detection and demodulation is proposed for generalized frequency division multiplexing with index modulation (GFDM-IM) scheme in order to improve the error performance of the system. The proposed method first pre-processes the received signal by using a zero-forcing (ZF) detector and then uses a neural network consisting of a convolutional neural network (CNN) followed by a fully-connected neural network (FCNN). The FCNN part uses only two fully-connected layers, which can be adapted to yield a trade-off between complexity and bit error rate (BER) performance. This two-stage approach prevents the getting stuck of neural network in a saddle point and enables IM blocks processing independently. It has been demonstrated that the proposed deep convolutional neural network-based detection and demodulation scheme provides better BER performance compared to ZF detector with a reasonable complexity increase. We conclude that non-orthogonal waveforms combined with IM schemes with the help of deep learning is a promising physical layer (PHY) scheme for future wireless networks
