Intelligent massive MIMO systems for beyond 5G networks: An overview and future trends

• Main Authors: Elijah, Olakunle, Abdul Rahim, Sharul Kamal, Wee, Kiat New, Chee, Yen Leow, Cumanan, Kanapathippilla, Tan, Kim Geok
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/104423/1/SharulKamalAbdul2022_IntelligentMassiveMIMOSystems.pdf; http://eprints.utm.my/104423/; http://dx.doi.org/10.1109/ACCESS.2022.3208284
• Institution: Universiti Teknologi Malaysia

Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output (CM-MIMO) systems. This introduces the concept of intelligent massive MIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO-enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.