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

被引：8

作者：

Iliadis, Lazaros Alexios ^{[1
]}

Zaharis, Zaharias D. ^{[3
]}

Sotiroudis, Sotirios ^{[1
]}

Sarigiannidis, Panagiotis ^{[2
]}

Karagiannidis, George K. ^{[3
]}

Goudos, Sotirios K. ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Sch Phys, ELEDIA AUTH, Thessaloniki, Greece

[2] Univ Western Macedonia, Dept Informat & Telecommun Engn, Kozani, Greece

[3] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, Thessaloniki, Greece

来源：

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING | 2022年 / 2022卷 / 01期

基金：

欧盟地平线“2020”;

关键词：

Cell-free massive MIMO; Deep learning; User-centric cell-free massive MIMO; 6G; POWER ALLOCATION;

D O I：

10.1186/s13638-022-02153-z

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

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.

引用

页数：16

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