Cell-Free Massive MIMO With Segmented Fronthaul: Reliability and Protection Aspects

Fernandes, Andre Lucas Pinho; Souza, Daynara D.; Costa, Daniel Benevides da; Cavalcante, André; Costa, João Crisóstomo Weyl Albuquerque

doi:10.1109/lwc.2022.3166485

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…GPP MTBF and repair time metrics are sourced from server node failure data in large-scale computational clusters [46]. Other values are derived from analogous components in different network types [44], [47]. Outdoor fiber MTBF scales with fiber length, which can be obtained as in [48] for a block scenario.…”

Section: ) Installation and Repair Assumptionsmentioning

confidence: 99%

A Cost Assessment Methodology for User-Centric Distributed Massive MIMO Architectures

Fernandes,

Souza,

Natalino

et al. 2024

IEEE Open J. Commun. Soc.

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User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), also known as cell-free mMIMO, is a pivotal technology for enabling future mobile communication systems. While UC D-mMIMO intrinsically follows a distributed architecture, its processing can be implemented in a distributed or centralized fashion. This paper proposes a comprehensive cost assessment methodology for UC D-mMIMO, capturing its total cost of ownership and factoring in the deployment configuration, processing implementation, computational demands, and fronthaul signaling. The methodology considers two transmission reception point (TRP) deployment strategies. The first focuses only on supporting user equipment (UE) demands, while the other fulfills these requirements and also actively strives to provide a fairer service among UEs. The proposed methodology is then used to perform a techno-economic assessment of the feasibility of centralized versus distributed processing functional splits while varying key costs and TRP capabilities, like antenna and served UE count. Results suggest that with the TRP deployment that only supports the required UE rate, distributed processing is usually the most feasible option for UE demands of up to 50 Mbps, and centralized processing is more cost-effective in other cases. Additionally, when considering the actively fairer TRP deployment, centralized processing becomes cheaper for any UE demands.INDEX TERMS Cell-free massive MIMO, feasibility analysis, network deployment, functional splits, techno-economic assessment, total cost of ownership.

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Section: ) Installation and Repair Assumptionsmentioning

confidence: 99%

A Cost Assessment Methodology for User-Centric Distributed Massive MIMO Architectures

Fernandes,

Souza,

Natalino

et al. 2024

IEEE Open J. Commun. Soc.

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show abstract

“…In [12], authors compare the performance of distributed and centralized precoding with limited FH capacity in DL D-MIMO setup. Authors in [14] consider a point-to-multipoint FH topology where an RU subset shares a serial FH link with limited capacity and develops iterative power control and RU scheduling algorithm. [15] analyzes the impact of individual and cumulative failures on RUs and FH segments in D-MIMO with segmented FH using Markov chains.…”

Section: A Related Workmentioning

confidence: 99%

Distributed MIMO Precoding with Routing Constraints in Segmented Fronthaul

Sadreddini,

Haliloglu,

Reial

2023

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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Distributed Multiple-Input and Multiple-Output (D-MIMO) is envisioned to play a significant role in future wireless communication systems as an effective means to improve coverage and capacity. In this paper, we have studied the impact of a practical two-level data routing scheme on radio performance in a downlink D-MIMO scenario with segmented fronthaul. At the first level, a Distributed Unit (DU) is connected to the Aggregating Radio Units (ARUs) that behave as cluster heads for the selected serving RU groups. At the second level, the selected ARUs connect with the additional serving RUs. At each route discovery level, RUs and/or ARUs share information with each other. The aim of the proposed framework is to efficiently select serving RUs and ARUs so that the practical data routing impact for each User Equipment (UE) connection is minimal. The resulting postrouting Signal-to-Interference plus Noise Ratio (SINR) among all UEs is analyzed after the routing constraints have been applied. The results show that limited fronthaul segment capacity causes connection failures with the serving RUs of individual UEs, especially when long routing path lengths are required. Depending on whether the failures occur at the first or the second routing level, a UE may be dropped or its SINR may be reduced. To minimize the DU-ARU connection failures, the segment capacity of the segments closest to the DU is set as double as the remaining segments. When the number of active co-scheduled UEs is kept low enough, practical segment capacities suffice to achieve a zero UE dropping rate. Besides, the proper choice of maximum path length setting should take into account segment capacity and its utilization due to the relation between the two.

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“…We compared grid and linear deployments of APs considering the cases of singleantenna APs and multi-antenna APs. The performance of a grid deployment and a linear deployment of APs in the downlink of an indoor industrial scenario was also compared in [13], where the authors investigated the effects of isolated and cumulative failures on the hardware of APs. They also proposed protection schemes that strongly or entirely mitigate the effects of these failures.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems

Tominaga

López

Souza

et al. 2022

2022 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

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The Fifth Generation (5G) of wireless networks introduced native support for Machine-Type Communication (MTC), which is a key enabler for the Internet of Things (IoT) revolution. Current 5G standards are not yet capable of fully satisfying the requirements of critical MTC (cMTC) and massive MTC (mMTC) use cases. This is the main reason why industry and academia have already started working on technical solutions for beyond-5G and Sixth Generation (6G) networks. One technological solution that has been extensively studied is the combination of network densification, massive Multiple-Input Multiple-Output (mMIMO) systems and user-centric design, which is known as distributed mMIMO or Cell-Free (CF) mMIMO. Under this new paradigm, there are no longer cell boundaries: all the Access Points (APs) on the network cooperate to jointly serve all the devices. In this paper, we compare the performance of traditional mMIMO and different distributed mMIMO setups, and quantify the macro diversity and signal spatial diversity performance they provide. Aiming at the uplink in industrial indoor scenarios, we adopt a path loss model based on real measurement campaigns. Monte Carlo simulation results show that the grid deployment of APs provide higher average channel gains, but radio stripes deployments provide lower variability of the received signal strength.

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Cell-Free Massive MIMO With Segmented Fronthaul: Reliability and Protection Aspects

Cited by 6 publications

References 10 publications

A Cost Assessment Methodology for User-Centric Distributed Massive MIMO Architectures

A Cost Assessment Methodology for User-Centric Distributed Massive MIMO Architectures

Distributed MIMO Precoding with Routing Constraints in Segmented Fronthaul

Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems

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