Deep Learning Based MIMO Transmission with Precoding and Radio Transformer Networks

Wenqi Cui, Anming Dong, Yi Cao, Chuanting Zhang, Jiguo Yu, Sufang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


In this paper, we study MIMO transmission schemes based on deep learning (DL). We propose a novel DL-based MIMO communication structure by combing a beamforming network at the transmitter side and a radio transformer network (RTN) at the receiver side. Compared with the classical DL-based MIMO communication systems, the interference is potentially mitigated by a precoding network and a RTN network, which is thus beneficial to improve the performance of signal detection. Simulation results show that the proposed scheme outperforms the classical MIMO transmission schemes in terms of bit error rate (BER).
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Number of pages6
StatePublished - Jun 12 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-01
Acknowledgements: This work was supported in part by the National Key R&D Program of China under grant 2019YFB2102600, the National Natural Science Foundation of China (NSFC) under Grants 61701269, 61832012, 61771289 and 61672321, the Shandong Provincial Natural Science Foundation under Grant ZR2017BF012, the Key Research and Development Program of Shandong Province under Grants 2019JZZY010313 and 2019JZZY020124, the Joint Research Fund for Young Scholars in Qilu University (Shandong Academy of Sciences) under Grant 2017BSHZ005.


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