The Application of Artificial Intelligence in Emerging MIMO Technologies
- DOI
- 10.2991/978-94-6463-518-8_73How to use a DOI?
- Keywords
- AI; Massive MIMO; CF Massive MIMO; Holographic MIMO; beamforming optimization; machine learning
- Abstract
Artificial intelligence (AI) technology in emerging Multiple-Input Multiple-Output (MIMO) systems is becoming a crucial direction for future wireless networks. This article explores the application of AI technology in emerging MIMO technology. Firstly, this article provides a concise overview of the historical development of MIMO technology, highlighting three key technologies: Massive MIMO, Holographic MIMO, and Cell-Free (CF) Massive MIMO. Subsequently, it reviews the latest advancements in each, focusing on key technologies and the applications of AI, such as machine learning. Compared to traditional algorithms, the model's accuracy, efficiency, and precision have seen significant enhancements, thereby boosting system performance. However, existing research faces challenges in interference mitigation, computational complexity, and practical deployment. Lastly, this article anticipates future research trajectories in this domain and envisions the potential of AI to drive innovative advancements in wireless communication systems.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shuai Zhang PY - 2024 DA - 2024/09/28 TI - The Application of Artificial Intelligence in Emerging MIMO Technologies BT - Proceedings of the 2024 International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2024) PB - Atlantis Press SP - 760 EP - 769 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-518-8_73 DO - 10.2991/978-94-6463-518-8_73 ID - Zhang2024 ER -