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Learning based MIMO communications with imperfect channel state information for Internet of Things

  • 1194: Secured and Efficient Convergence of Artificial Intelligence and Internet of Things
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Abstract

Imperfect channel state information (CSI) may seriously worsen the system performance for classical MIMO communications. In order to overcome the impacts of imperfect CSI for Internet of things, we propose a deep convolutional neural network (DCNN) based MIMO detection algorithm, where the DCNN is trained offline and works online to refine the imperfect CSI and improve the bit error rate of the wireless systems. Two types of learning based detectors, i.e., with or without accurate CSI, are proposed in this paper to reduce the detrimental effects of imperfect CSI. The impacts of the important system parameters, such as normalized Doppler frequency and the correlation factor are evaluated in different setup scenarios. Simulation results suggest that, compared with the classical maximum likelihood detector, the proposed learning based detectors shows considerable gains.

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Acknowledgments

This work was partly supported by Natural Science Foundation of Guangdong Province with grant number 2018A030313736, Scientific Research Project of Education Department of Guangdong with grant number 2019GZDXM002, Application Technology Collaborative Innovation Center of GZPYP with grant number 2020ZX01, Yangcheng scholar, scientific research project of Guangzhou Education Bureau with grant number 202032761, Project of Technology Development Foundation of Guangdong with grant number 706049150203, the National Natural Science Foundation of China Grant 62001320, Key Scientific Research Projects of Higher Education Institutions in Henan Province Grant 20A510007, the Natural Science Foundation of Shaanxi Province under Grant 2020JQ-844, the Fundamental Research Funds for the Universities of Henan Province under Grant NSFRF180309, the Key Research and Development Program of Shanxi under Grant 201903D121117.

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Authors and Affiliations

  1. School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China

    Dan Deng

  2. School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, 454150, China

    Xingwang Li

  3. Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Ernakulam, 683576, India

    Varun G. Menon

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  1. Dan Deng
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  2. Xingwang Li
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  3. Varun G. Menon
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Correspondence to Xingwang Li.

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Deng, D., Li, X. & Menon, V.G. Learning based MIMO communications with imperfect channel state information for Internet of Things. Multimed Tools Appl 80, 31265–31276 (2021). https://doi.org/10.1007/s11042-020-10387-6

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