@article{articlec,

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

author = {Lazaros Alexios Iliadis and Zaharias Zaharis and Sotirios Sotiroudis and Panagiotis Sarigiannidis and George Karagiannidis and Sotirios Goudos},

url = {https://www.researchgate.net/publication/362590698_The_road_to_6G_a_comprehensive_survey_of_deep_learning_applications_in_cell-free_massive_MIMO_communications_systems},

doi = {10.1186/s13638-022-02153-z},

year  = {2022},

date = {2022-08-01},

journal = {EURASIP Journal on Wireless Communications and Networking},

volume = {2022},

abstract = {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.},

keywords = {6G, Cell-free massive MIMO, Deep Learning, User-centric cell-free massive MIMO},

pubstate = {published},

tppubtype = {article}

}