Deep Learning Based MIMO Transmission with Precoding and Radio Transformer Networks
Online Publication Date2021-06-12
Print Publication Date2021
Permanent link to this recordhttp://hdl.handle.net/10754/670864
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AbstractIn 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).
CitationCui, W., Dong, A., Cao, Y., Zhang, C., Yu, J., & Li, S. (2021). Deep Learning Based MIMO Transmission with Precoding and Radio Transformer Networks. Procedia Computer Science, 187, 396–401. doi:10.1016/j.procs.2021.04.078
SponsorsThis 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.
Conference/Event name9th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2020
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license.