University of Oulu

M. A. Albreem, A. H. Alhabbash, S. Shahabuddin and M. Juntti, "Deep Learning for Massive MIMO Uplink Detectors," in IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 741-766, Firstquarter 2022, doi: 10.1109/COMST.2021.3135542

Deep learning for massive MIMO uplink detectors

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Author: Albreem, Mahmoud A.1; Alhabbash, Alaa2; Shahabuddin, Shahriar3,4;
Organizations: 1Department of Electrical Engineering, University of Sharjah, Sharjah 27272, UAE
2Palestinian ICT Research Agency, Gaza, Palestine
3Mobile Networks, Nokia, Oulu 90620, Finland
4Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-01-10


Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers’ performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector’s architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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Series: IEEE communications surveys and tutorials
ISSN: 1553-877X
ISSN-E: 2373-745X
ISSN-L: 1553-877X
Volume: 24
Issue: 1
Pages: 741 - 766
DOI: 10.1109/COMST.2021.3135542
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research has been financially supported in part by the University of Sharjah under Seed Research Grant, and in part by the Research Council (TRC) of the Sultanate of Oman (agreement No. TRC/BFP/ASU/01/2018).
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