Deep Learning-Assisted OFDM Channel Estimation and Signal Detection Technology
Jun Li, Zhichen Zhang, Yu–Kai Wang, Bo He, Wenjing Zheng, Mingming Li
Abstract
The orthogonal frequency division multiplexing (OFDM) technique has received wide attention because of its high spectrum utilization. However, 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. In this letter, 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). The simulation results show that the SCBiGNet model outperforms the existing techniques for the different numbers of pilots and lengths of CPs. The BER performance is improved by 0.2-9 dB.