University of Oulu

L. V. Nguyen, N. T. Nguyen, N. H. Tran, M. Juntti, A. L. Swindlehurst and D. H. N. Nguyen, "Leveraging Deep Neural Networks for Massive MIMO Data Detection," in IEEE Wireless Communications, doi: 10.1109/MWC.013.2100652

Leveraging deep neural networks for massive MIMO data detection

Saved in:
Author: Nguyen, Ly V.1; Nguyen, Nhan T.2; Tran, Nghi H.3;
Organizations: 1Computational Science Research Center, San Diego State University, San Diego, CA, USA 92182
2Centre for Wireless Communications, University of Oulu, P.O.Box 4500, FI-90014, Finland
3Department of Electrical and Computer Engineering, University of Akron, OH, USA 44325 USA
4Center for Pervasive Communications and Computing, Henry Samueli School of Engineering, University of California, Irvine, CA, USA 92697
5Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, USA 92182
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-01-26


Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

see all

Series: IEEE wireless communications
ISSN: 1536-1284
ISSN-E: 1558-0687
ISSN-L: 1536-1284
Issue: Online first
DOI: 10.1109/mwc.013.2100652
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work of L. V. Nguyen and A. L. Swindlehurst was supported in part by the National Science Foundation under grant ECCS-18245. The work of N. T. Nguyen and M. Juntti was supported in part by Infotech Oulu Focus Institure, the Academy of Finland, 6G Flagship program under Grant 346208 and project EERA under Grant 332362. The work by N. H. Tran was supported by the University of Akron’s 2022 Faculty Research Committee (FRC) Fellowship Award.
Academy of Finland Grant Number: 346208
Detailed Information: 346208 (Academy of Finland Funding decision)
332362 (Academy of Finland Funding decision)
Copyright information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.