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Data-Rate Driven Transmission Strategies for Deep Learning Based Communication Systems

Chen, Xiao, Cheng, Julian, Zhang, Zaichen, Wu, Liang, Dang, Jian, Wang, Jiangzhou (2020) Data-Rate Driven Transmission Strategies for Deep Learning Based Communication Systems. IEEE Transactions on Communications, . ISSN 0090-6778. (doi:10.1109/TCOMM.2020.2968314) (KAR id:80622)


Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.

Item Type: Article
DOI/Identification number: 10.1109/TCOMM.2020.2968314
Uncontrolled keywords: Adaptive modulation, Adaptive signal processing, Communication, Communication systems, Artificial intelligence, Signal processing
Subjects: Q Science
T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Jiangzhou Wang
Date Deposited: 26 Mar 2020 11:07 UTC
Last Modified: 09 Dec 2022 05:16 UTC
Resource URI: (The current URI for this page, for reference purposes)

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