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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62364

A Taxonomy and Review and Machine Learning Enabled MIMO Systems for Wireless Networks

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62364

Volume:

10 |

5 |

May 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

HP

Harsh Patidar

Electronics and Communication Engineering

SDBCT, Indore

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AS

Amit Sharma

Electronics and Communication Engineering

SDBCT, Indore

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NS

Neelam Sharma

Electronics and Communication Engineering

SDBCT, Indore

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Abstract

Due to increasing number of users and multimedia applications, multiple input, multiple output (MIMO) systems have become indispensable from wireless technology. The benefit of MIMO systems is increase in the channel capacity at low bandwidths, however, the accurate reception of the MIMO signal at the receiver is extremely complex. This is due to the addition of noise and interference, as well as the different behavior of the wireless channel for the different channel paths. Due to the largeness and the complexity of the data, the technique used off late is the Deep Learning based approach for signal detection. This paper presents the basics of MIMO systems and insight into deep learning based MIMO channel estimation and signal detection techniques.

KEYWORDS

Keywords— MIMO Deep Learning Channel Response Artificial Neural Network (ANN) Deep Neural Network Mean Square Error (MSE).

Cite This Article

Harsh Patidar, Amit Sharma, Neelam Sharma, "A Taxonomy and Review and Machine Learning Enabled MIMO Systems for Wireless Networks," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-9, May 2026.

Full Article/Conclusion

It can be concluded that MIMO systems and moreover massive MIMO systems are the only way out to provide limited bandwidth yet bandwidth hungry applications. Massive MIMO may serve as a technique to increase the system capacity and date rate.  However, MIMO  systems suffer from distortion effects. This is due to the addition of noise and interference, as well as the different behavior of the wireless channel  for the different channel paths. Due to the largeness and the complexity of the data, the technique used off late is the Deep Learning based approach for signal detection. This paper presents a comprehensive review of machine  learning and deep learning assisted massive MIMO systems along with the salient features of the approaches. It is expected that the paper will serve as an effective tool to find future directions of research.

References

1.                M. A. Jensen and J. W. Wallace, "A review of antennas and propagation for MIMO wireless communications," in IEEE Transactions on Antennas and Propagation, vol. 52, no. 11, pp. 2810-2824, Nov. 2004, doi: 10.1109/TAP.2004.835272. 2.                N. Kim, Y. Lee and H. Park, "Performance Analysis of MIMO System with Linear MMSE Receiver," in IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4474-4478, November 2008, doi: 10.1109/T-WC.2008.070785. 3.                A. H. Mehana and A. Nosratinia, "Diversity of MMSE MIMO Receivers," in IEEE Transactions on Information Theory, vol. 58, no. 11, pp. 6788-6805, Nov. 2012, doi: 10.1109/TIT.2012.2204954. 4.                M. Biguesh and A. B. Gershman, "Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals," in IEEE Transactions on Signal Processing, vol. 54, no. 3, pp. 884-893, March 2006, doi: 10.1109/TSP.2005.863008. 5.                A. Ghazal et al., "A Non-Stationary MIMO Channel Model for High-Speed Train Communication Systems," 2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012, pp. 1-5, doi: 10.1109/VETECS.2012.6240333. 6.                H. Shin and M. Z. Win, "MIMO Diversity in the Presence of Double Scattering," in IEEE Transactions on Information Theory, vol. 54, no. 7, pp. 2976-2996, July 2008, doi: 10.1109/TIT.2008.924672. 7.                M. Damrath, H. B. Yilmaz, C. Chae and P. A. Hoeher, "Array Gain Analysis in Molecular MIMO Communications," in IEEE Access, vol. 6, pp. 61091-61102, 2018, doi: 10.1109/ACCESS.2018.2875925. 8.                Q. He, Z. Wang, J. Hu and R. S. Blum, "Performance Gains From Cooperative MIMO Radar and MIMO Communication Systems," in IEEE Signal Processing Letters, vol. 26, no. 1, pp. 194-198, Jan. 2019, doi: 10.1109/LSP.2018.2880836. 9.                F. Rusek and A. Prlja, "Optimal Channel Shortening for MIMO and ISI Channels," in IEEE Transactions on Wireless Communications, vol. 11, no. 2, pp. 810-818, February 2012, doi: 10.1109/TWC.2011.121911.110809. 10.             A. Abdelaziz and C. E. Koksal, "Fundamental limits of covert communication over MIMO AWGN channel," 2017 IEEE Conference on Communications and Network Security (CNS), 2017, pp. 1-9, doi: 10.1109/CNS.2017.8228657.

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Publication Date

5/5/2026

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