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Review
. 2021 Dec 31;22(1):309.
doi: 10.3390/s22010309.

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Muddasar Naeem  1 , Giuseppe De Pietro  1 , Antonio Coronato  1
Affiliations
Review

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Muddasar Naeem et al. Sensors (Basel). .

Abstract

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

Keywords: BS; CSI; MIMO systems; channel estimation; deep learning; detection communication; localization; mmWave communication; positioning; reinforcement learning; resource allocation; signal.

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Conflict of interest statement

Authors declares no conflict of interest.

Figures

Figure 1
Figure 1
The reinforcement learning problem.
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Figure 2
MIMO communication.
Figure 3
Figure 3
Number of papers from 2010 to 2021.
Figure 4
Figure 4
Number of papers surveyed by category.
Figure 5
Figure 5
Distribution of the surveyed papers with respect to the different categories.
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Figure 6
Percentage of papers surveyed by DL architecture.
Figure 7
Figure 7
Percentage of papers surveyed by RL algorithm.
Figure 8
Figure 8
Number of citations by category.
See this image and copyright information in PMC

References

    1. Rusek F., Persson D., Lau B.K., Larsson E.G., Marzetta T.L., Edfors O., Tufvesson F. Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Process. Mag. 2012;30:40–60. doi: 10.1109/MSP.2011.2178495. - DOI
    1. Larsson E.G., Edfors O., Tufvesson F., Marzetta T.L. Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 2014;52:186–195. doi: 10.1109/MCOM.2014.6736761. - DOI
    1. Marzetta T.L. Massive MIMO: An introduction. Bell Labs Tech. J. 2015;20:11–22. doi: 10.15325/BLTJ.2015.2407793. - DOI
    1. Sutton R.S., Barto A.G. Introduction to Reinforcement Learning. Volume 135 MIT Press; Cambridge, MA, USA: 1998.
    1. Kormushev P., Calinon S., Caldwell D. Reinforcement Learning in Robotics: Applications and Real-World Challenges. Robotics. 2013;2:122–148. doi: 10.3390/robotics2030122. - DOI

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