Deep Learning Based End-to-End Wireless Communication Systems Without Pilots

• Published in: IEEE transactions on cognitive communications and networking Vol. 7; no. 3; pp. 702 - 714
• Main Authors: Ye, Hao, Li, Geoffrey Ye, Juang, Biing-Hwang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Channel estimation; Channels; Communication; Communication systems; Data recovery; Deep learning; Feature extraction; Information retrieval; joint source channel coding; Machine learning; MIMO communication; Modules; Parameter estimation; pilot-free end-to-end communications; Receivers; Transmitters; Wireless communication; Wireless communication systems; Wireless communications
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2021.3061464

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.