正交频分复用
副载波
计算机科学
循环前缀
频道(广播)
卷积神经网络
干扰(通信)
信号(编程语言)
深度学习
频分复用
传输(电信)
人工智能
实时计算
算法
电子工程
电信
工程类
程序设计语言
作者
Jun Li,Zhichen Zhang,Yu–Kai Wang,Bo He,Wenjing Zheng,Mingming Li
标识
DOI:10.1109/lcomm.2023.3245807
摘要
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.
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