A review on deep learning aided pilot decontamination in massive MIMO

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.

Keywords:

• Pilot contamination
• channel estimation
• deep learning
• deep neural networks
• pilot assignment and design
• massive MIMO

REVIEWING EDITOR:

• Qingsong Ai, Wuhan University of Technology, Wuhan, China

Subjects:

• Artificial Intelligence
• Engineering Education
• Technology
• Electrical & Electronic Engineering
• Electromagnetics & Communication

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work did not receive any funding.

Notes on contributors

Crallet M. Victor

Crallet M. Victor received the B.Sc. and M.Sc. degrees in Telecommunications engineering from the University of Dar es Salaam, Tanzania in 2009 and the University of Dodoma, Tanzania in 2012 respectively. Currently, he is a PhD student in Telecommunications Engineering at the Department of Electronics and Telecommunications Engineering, College of Informatics and Virtual Education, University of Dodoma, Dodoma, Tanzania. He is also a Lecturer in the Department of Electronics and Telecommunications Engineering, College of Informatics and Virtual Education, University of Dodoma, Dodoma, Tanzania. His research interest includes wireless communication, deep learning for communication systems, multimedia systems, and electronics engineering. Email: [email protected], [email protected]

Alloys N. Mvuma

Aloys N. Mvuma received a BSc in Electrical Engineering degree from the University of Dar es Salaam in 1994, an MSc in Information Science from Shimane University, Japan, and a Doctor of Engineering (Systems Engineering) from Hiroshima University, Japan in 2003. He is currently an Associate Professor at Mbeya University of Science and Technology. His research interests include adaptive signal processing, digital communication systems, and information communication technologies for development. He has published over 20 IEEE conference papers and various journal papers. He is a registered member of the Engineers Registration Board (ERB) and the Institute of Electrical and Electronic Engineers (IEEE). Email: [email protected]

Salehe I. Mrutu

Salehe I. Mrutu received his B.Sc. and M.Sc. degrees in Computer Science from the International University of Africa (IUA) in 2003 and the University of Gezira in 2006 respectively. In 2014, He obtained his PhD in Information and Communication Science and Engineering from the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. He is currently serving as a lecturer at The University of Dodoma (UDOM) under the College of Informatics and Virtual Education. His research interests include signal processing, artificial intelligence, forward error correction (FEC) codes, quality-of-service provisioning, and resource management for multimedia communications networks. Email: [email protected]