A review on deep learning aided pilot decontamination in massive MIMO
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Informa UK Limited
Abstract
In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output
(MIMO), beamforming, and beam selection depend heavily on the accurate acquisition
of the channel state. However, pilot contamination (PC) can be a major source of interference
which degrades they are performance. Moreover, the severity of PC increases as more
pilots are reused between users in the wireless systems. Researchers have shown that PC
can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing
PC, the examination that identifies the applications and factors that distinguish these DL
approaches is still limited. This paper reviews these DL approaches and the improvements
needed to enhance their performance. Simulation results confirm that DL networks that
learn to predict the channels directly have superior performance under PC.
Description
Full text. Available at https://doi.org/10.1080/23311916.2024.2322822
Keywords
Citation
Victor, C. M., Mvuma, A. N., & Mrutu, S. I. (2024). A review on deep learning aided pilot decontamination in massive MIMO. Cogent Engineering, 11(1), 2322822.