A review on deep learning aided pilot decontamination in massive MIMO

• Published in: Cogent engineering 2024-12, Vol.11 (1)
• Main Authors: Victor, Crallet M., Mvuma, Alloys N., Mrutu, Salehe I.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Beamforming; channel estimation; Decontamination; Deep learning; deep neural networks; Distance learning; Education; Electrical & Electronic Engineering; Electromagnetics & Communication; Engineering; Engineering Education; Informatics; massive MIMO; MIMO communication; Neural networks; pilot assignment and design; Pilot contamination; Pilots; Qingsong Ai, Wuhan University of Technology, Wuhan, China; Technology
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2024.2322822

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.