Reducing Inter-CPU Coordination in User-Centric Distributed Massive MIMO Networks

Freitas, Marx; Souza, Daynara D.; Costa, Daniel Benevides da; Cavalcante, André; Valcarenghi, L.; Borges, Gilvan; Rodrigues, Roberto; Costa, João Crisóstomo Weyl Albuquerque

doi:10.1109/lwc.2023.3252000

Cited by 5 publications

(16 citation statements)

References 12 publications

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“…The study proved that centralized processing can be scalable, cementing its position as the best processing approach. Expanding on the framework, [7] identified that an increasing number of TRPs might reintroduce non-scalability. Accordingly, it complemented the TRPs cluster formation to guarantee scalability under such conditions.…”

Section: A Literature Review 1) Uc D-mmimomentioning

confidence: 99%

“…A dynamic cooperation clustering (DCC) framework is used to manage the UE/TRP connections and assure that the computational complexity of channel estimation and signal processing will be limited even if L or K grows to infinity. The initial access procedure from [6] is executed combined with the TRP cluster size control technique outlined in [7]. This strategy ensures that each TRP establishes connections with a maximum of τ p UEs, and correspondingly, each UE forms connections with up to U max TRPs.…”

Section: ) System Scalabilitymentioning

confidence: 99%

“…Table 11 summarizes the system simulation parameters. Most are selected based on parameters commonly adopted in the literature [4], [7], [40], [41]. The number of antennas per TRP is chosen to represent the simplest TRP with multi-antenna processing capabilities.…”

Section: ) System Model Assumptionsmentioning

confidence: 99%

“…Nevertheless, this does not mean centralized approaches are always superior. The computational complexity can be orders of magnitude higher than the distributed case [7]. Besides that, the fronthaul requirements can be higher in the centralized case depending on the antenna count on the TRP, its supported number of UEs, and the adequate sample bit width for the supported UE data rate [5], [8].…”

Section: Introductionmentioning

confidence: 99%

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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: A Literature Review 1) Uc D-mmimomentioning

confidence: 99%

Section: ) System Scalabilitymentioning

confidence: 99%

Section: ) System Model Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…This is performed in order to comply with the scalability requirements and to reduce the processing complexity from the network [5], [26], [27]. Let s k ∈ C denote the symbol intended for the UE k. The DL received signal at the UE k can be given by…”

Section: B Downlink Data Transmissionmentioning

confidence: 99%

Trajectory Optimization in User-Centric Distributed Massive MIMO Systems Enabled by UAV Swarms

Souza,

Freitas,

Fernandes

et al. 2024

Preprint

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User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), commonly called cell-free mMIMO, is an important technology to ensure a more uniform coverage as well as higher spectral and energy efficiencies in next generation communication systems. This paper investigates the performance of UC D-mMIMO systems enabled by a swarm of unmanned aerial vehicles (UAVs). Specifically, it presents a comprehensive study on UAVs’ deployment and trajectory optimization as aerial transmission and reception points (TRPs) of D-mMIMO systems, considering systems composed solely of aerial TRPs and those formed combining aerial and terrestrial TRPs. Moreover, user equipment (UE) mobility is modeled using a discrete-time Markov chain, and a novel approach to heuristically optimize the positions of aerial TRPs is proposed, one that considers the continuous movement of UEs in the coverage area. The proposed approach optimizes each UAV’s three-dimensional location under a time discretization framework, with the positioning of the UAVs being adjusted periodically, allowing for iterative trajectory optimization to improve the UEs’ spectral efficiency (SE) performance. Simulation results reveal that the proposed UAV trajectory optimization allows for significant SE improvement, especially for a low UE density scenario. Specifically, comparing the proposed method with a fixed position setup, up to 47.84% increase on average SE is achieved.

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Scalable User-Centric Distributed Massive MIMO Systems with Limited Processing Capacity

Freitas,

Souza,

Fernandes

et al. 2023

ICC 2023 - IEEE International Conference on Communications

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Reducing Inter-CPU Coordination in User-Centric Distributed Massive MIMO Networks

Cited by 5 publications

References 12 publications

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

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

Trajectory Optimization in User-Centric Distributed Massive MIMO Systems Enabled by UAV Swarms

Scalable User-Centric Distributed Massive MIMO Systems with Limited Processing Capacity

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