User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Ammar, Hussein A.; Adve, Raviraj; Shahbazpanahi, Shahram; Boudreau, Gary; Srinivas, Kothapalli Venkata

doi:10.1109/comst.2021.3135119

Cited by 229 publications

(85 citation statements)

References 249 publications

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“…Moreover, with the emergence of new materials and technologies, such as AI, reconfigurable intelligent surfaces, sensing technologies, and ultra-large aperture array, Extreme MIMO technology in 6G can overcome the high transmission loss in the THz frequency band and achieve higher spectral efficiency and higher energy efficiency in a wider frequency range, and apply to more scenarios, such as macro coverage, hotspot coverage, 3D coverage, high-speed mobility, and accurate positioning as well. However, extreme MIMO still faces many challenges, such as inaccurate channel measurement and modeling, the burden of signal processing load, limited fronthaul capacity, high reference signal overhead, and high costs [36].…”

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

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Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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show abstract

“…This scheme is simple to implement. However, in the cell-free massive MIMO system, the APs will not be equipped with a large-scale antenna array, so the channel hardening effect is not significant [ 26 , 29 , 30 ]. The performance loss is large.…”

Section: System Modelmentioning

confidence: 99%

Energy Efficiency of User-Centric, Cell-Free Massive MIMO-OFDM with Instantaneous CSI

Han

Zhao

2022

Entropy

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In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.

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“…To address this disadvantage, the user-centric cell-free massive MIMO concept appeared [ 16 , 17 ]. The user-centric model was proposed in [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System

Shin

Kim

et al. 2022

Sensors

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A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.

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User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Cited by 229 publications

References 249 publications

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Energy Efficiency of User-Centric, Cell-Free Massive MIMO-OFDM with Instantaneous CSI

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System

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