In this letter, we propose a deep learning-assisted approach for signal detection in uplink orthogonal frequency-division multiplexing (OFDM) systems over time-varying channels. In particular, we utilize a recurrent neural network (RNN) with bidirectional long short-term memory (LSTM) architecture to achieve signal detection. In addition, with the help of convolutional neural network (CNN) and batch normalization (BN), a new network structure CNN-BN-RNN Network (CBR-Net) is proposed to obtain better performance. The sequence feature information of the OFDM received signal is extracted from big data to successfully train a RNN-based signal detection model, which simplifies the architecture of OFDM systems and can adapt to the change of channel paths. Simulation results also demonstrate that the trained RNN model has the ability to recall the characteristics of wireless time-varying channels and provide accurate and robust signal recovery performance.

中文翻译：

---

使用 RNN 和双向 LSTM 在上行链路时变 OFDM 系统中进行信号检测

在这封信中，我们提出了一种深度学习辅助方法，用于时变信道上行链路正交频分复用 (OFDM) 系统中的信号检测。特别是，我们利用具有双向长短期记忆 (LSTM) 架构的循环神经网络 (RNN) 来实现信号检测。此外，在卷积神经网络（CNN）和批量归一化（BN）的帮助下，提出了一种新的网络结构CNN-BN-RNN网络（CBR-Net）以获得更好的性能。从大数据中提取OFDM接收信号的序列特征信息，成功训练出基于RNN的信号检测模型，简化了OFDM系统的架构，能够适应信道路径的变化。