Cell-Free Massive MIMO for 6G Wireless Communication Networks

He, Hengtao; Yu, Xianghao; Zhang, Jun; Song, S. H.; Letaief, Khaled B.

doi:10.23919/jcin.2021.9663100

“…Therefore, 6G wireless technologies are being developed to address the limitations of 5G for IoT networks but also for other applications. In the field of M2M application, satisfying the latency, bandwidth, resilience, and reliability requirements, the 6G is supposed to provide better performance than 5G networks as well as increased performance metrics by implementing new technologies, such as multiple access [44], waveform design [44], channel coding schemes, multi-antenna technologies [45], cloud/edge/fog computing [1], cell-free architecture [46][47], zero-energy devices [3], network function virtualization (NFV) [48], network agility and intelligence [48], open radio access network (RAN) [49], and low backhaul and access network congestion [50]. In this context,…”

Section: A Novel Attributes Of 6g Communicationsmentioning

confidence: 99%

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Das¹,

Mudi²,

Rahman³

et al. 2023

Preprint

1

0

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

show abstract

“…Therefore, 6G wireless technologies are being developed to address the limitations of 5G for IoT networks but also for other applications. In the field of M2M application, satisfying the latency, bandwidth, resilience, and reliability requirements, the 6G is supposed to provide better performance than 5G networks as well as increased performance metrics by implementing new technologies, such as multiple access [44], waveform design [44], channel coding schemes, multi-antenna technologies [45], cloud/edge/fog computing [1], cell-free architecture [46][47], zero-energy devices [3], network function virtualization (NFV) [48], network agility and intelligence [48], open radio access network (RAN) [49], and low backhaul and access network congestion [50]. In this context, Table III presents novel attributes of 6G networks when compared with 5G networks.…”

Section: A Novel Attributes Of 6g Communicationsmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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“…The applicability of CF M-MIMO to 6G vision is thoroughly discussed in [3]. An extensive presentation of the foundations of user-centric CF M-MIMO is provided in [2].…”

Section: Related Workmentioning

confidence: 99%

“…A central processing unit (CPU) is connected with the APs via the backhaul network, while all users are served based on a cooperation between the APs which use time-division duplexing (TDD) mode. The main benefits over the classical cellular technology are: (i) smaller SNR variations, (ii) managing interference and (iii) increased SNR values [2][3][4][5][6].…”

mentioning

confidence: 99%

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

Iliadis

¹

,

Zaharis

²

,

Sotiroudis

³

et al. 2022

J Wireless Com Network

10

0

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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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Cell-Free Massive MIMO for 6G Wireless Communication Networks

Cited by 76 publications

References 89 publications

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

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

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