Deep Learning Based End-to-End Wireless Communication Systems Without Pilots
Deep Learning Based End-to-End Wireless Communication Systems Without Pilots
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DOI：
10.1109/tccn.2021.3061464
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发表时间：
2021-02
影响因子：
8.6
通讯作者：
Hao Ye;Geoffrey Y. Li;B. Juang
中科院分区：
文献类型：
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作者：
Hao Ye;Geoffrey Y. Li;B. Juang
机器学习的最新发展，特别是深度神经网络(DNN)的发展，使得基于学习的端到端通信系统成为可能，其中DNN被用来替代发送器和接收器的所有模块。本文提出了频率选择性信道和多输入多输出(MIMO)信道的两种端到端框架，其中无线信道效应用不可训练的随机卷积层来建模。端到端框架用小批次的输入数据和通道样本进行训练。与当前通信系统中使用导频信息隐式或显式地估计未知信道参数不同，发射机DNN学习以对各种信道条件稳健的方式来变换输入数据。接收器由两个DNN模块组成，分别用于信道信息提取和数据恢复。采用双线性乘积运算来组合从信道信息提取模块提取的特征和接收的信号。在数据恢复模块中还利用组合的特征来恢复传输的数据。与传统的通信系统相比，频率选择性信道和MIMO信道的性能都得到了改善。此外，端到端系统可以自动利用通道和源数据中的相关性来提高整体性能。
The recent development in machine learning, especially in deep neural networks (DNN), has enabled learning-based end-to-end communication systems, where DNNs are employed to substitute all modules at the transmitter and receiver. In this article, two end-to-end frameworks for frequency-selective channels and multi-input and multi-output (MIMO) channels are developed, where the wireless channel effects are modeled with an untrainable stochastic convolutional layer. The end-to-end framework is trained with mini-batches of input data and channel samples. Instead of using pilot information to implicitly or explicitly estimate the unknown channel parameters as in current communication systems, the transmitter DNN learns to transform the input data in a way that is robust to various channel conditions. The receiver consists of two DNN modules used for channel information extraction and data recovery, respectively. A bilinear production operation is employed to combine the features extracted from the channel information extraction module and the received signals. The combined features are further utilized in the data recovery module to recover the transmitted data. Compared with the conventional communication systems, performance improvement has been shown for frequency-selective channels and MIMO channels. Furthermore, the end-to-end system can automatically leverage the correlation in the channels and in the source data to improve the overall performance.