The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems

被引：0
作者：
Lazaros Alexios Iliadis
Zaharias D. Zaharis
Sotirios Sotiroudis
Panagiotis Sarigiannidis
George K. Karagiannidis
Sotirios K. Goudos
机构：
[1] Aristotle University of Thessaloniki,ELEDIA@AUTH, School of Physics
[2] University of Western Macedonia,Department of Informatics and Telecommunications Engineering
[3] Aristotle University of Thessaloniki,Department of Electrical and Computer Engineering
来源：
EURASIP Journal on Wireless Communications and Networking | / 2022卷
关键词：
Cell-free massive MIMO; Deep learning; User-centric cell-free massive MIMO; 6G;
D O I：
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学科分类号：
摘要：
The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.
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