SCBiGNet

The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet).

正交频分复用（OFDM）系统中子载波间干扰的缺陷导致仅有少量导频和较短循环前缀（CP）的OFDM系统在信道估计和信号检测性能上表现不佳。因此，我们采用深度学习技术辅助OFDM系统恢复非线性失真的传输数据。具体而言，我们利用自归一化网络（SNN）进行信道估计，并结合卷积神经网络（CNN）和双向门控循环单元（BiGRU）进行信号检测，从而提出了一种新型的SNN-CNN-BiGRU网络结构（SCBiGNet）。