Resilient BBU placement in 5G C-RAN over optical aggregation networks

Shehata, Mohamed; Musumeci, Francesco; Tornatore, Massimo

doi:10.1007/s11107-018-00826-8

Cited by 16 publications

(14 citation statements)

References 20 publications

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“…Deployment strategies for C-RAN have been proposed recently for Wavelenght Division Multiplexing (WDM) networks based on ILP and heuristic strategies [24,25]. Reliability aspects for C-RAN deployment are analyzed in detail in References [10,26,27]. In References [28], the authors propose a fog computing framework for C-RAN in vehicular networks.…”

Section: Introductionmentioning

confidence: 99%

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

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The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

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Section: Introductionmentioning

confidence: 99%

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

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“…Using the wireless multi-hop to transfer affected traffic in mm-wave accessing network in 5G C-RAN has also major shortcomings as: 1) traditional wireless routers do not have enough processing capacity for mm-wave signal transferring, and deploying mm-wave routers brings huge extra overhead; 2) mm-wave transmission is strongly directional and needs expensive pre-beamforming schemes for survivability management at each router. In 5G C-RAN, most existing researches on reliable network planning are focusing on the optimal BBU placement problem to protect against BBU failure [24], [25].…”

Section: A Prior Workmentioning

confidence: 99%

Joint Optimization of Survivability and Energy Efficiency in 5G C-RAN With mm-Wave Based RRH

Tian

Zhang

et al. 2020

IEEE Access

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Centralized Radio Access Networks (C-RAN) exploiting millimeter wave (mm-wave) technology in remote radio heads (RRHs) are regarded as a promising approach to satisfy the challenging service requirements of fifth generation (5G) mobile communication. However, ultra-dense deployment of mm-wave RRHs will generate enormous amount of traffic that will require effective design and operation of C-RAN backhaul. In this paper, we focus on developing an optimal mm-wave RRHs placement strategy that exploits resource and traffic assignment in RRHs to achieve reliable and energy efficient backhaul transmissions. Specifically, in this paper, mm-wave is considered both to provide end users access and to interconnect RRHs in same frequency band, hence achieving energy saving thanks to hardware and frequency reuse. In this scenario, leveraging the traffic predictions obtained by a deep neural network, we present a real-time traffic assignment scheme where traffic from affected RRHs can be rerouted to other RRHs to protect against backhaul failures and traffic migrates to as few RRHs as possible to switch off some backhaul links for energy efficiency. Due to the inherent short-range transmission of mm-wave, different RRH deployment locations significantly affect interconnections in RRHs. Therefore, we model the mm-wave RRH placement problem into an optimization framework that jointly maximizes backhaul survivability and energy efficiency, whilst subjects to constraints as network coverage and capacity. To guarantee scalability of the proposed scheme as network scale increases, a heuristic algorithm is also proposed. Numerical evaluations show that, with appropriate RRH placement strategies, significant survivability and energy efficiency improvements can be achieved. INDEX TERMS Centralized radio access network, backhaul link, millimeter wave, survivability, energy efficiency.

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“…The constraints of the problem can be categorised as well into three classes: network (13), (14), (21) : radius of the cell These three parameters have a different impact on the number of cells and the number of required BBUs. An increase of R S implies an increase of the number of cells, while an increase of R C reduces the number of cells.…”

Section: Integer Linear Programming Modelmentioning

confidence: 99%

“…Recently, relevant aspects related to C-RAN deployment have been investigated. In [13] survivability strategies for link and node failures in C-RAN are studied with different optimisation objectives. A novel technique to compute the optimal deployment of BBUs based on machine learning is proposed in [14] with the objective to minimise bandwidth and computing resources consumption.…”

Section: Introductionmentioning

confidence: 99%

Cost-effective joint Optimisation of BBU Placement and Fronthaul Deployment in Brown-field Scenarios

Marotta

Correia

2020

Preprint

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In this work one proposes a model to evaluate the optimal deployment of Centralised Radio Access Network (C-RAN) architecture elements, i.e. Base Band processing Units (BBUs) and fronthaul links, in a brownfield scenario in which traditional base stations are already deployed and a physical network is present. The proposed optimisation framework jointly optimises BBU placement and access network infrastructures deployment. It clusterises Remote Radio Heads in the scenario through a Multicommodity Flow approach and solves the minimum cost fronthaul network deployment through a Rooted Delay-Constrained Minimum Spanning Tree approach. Optical fibre and microwave links are considered as fronthaul infrastructures. The proposed optimisation framework is validated through a comparison with a theoretical output for a canonical scenario then it is applied to a real scenario. A cost analysis for different scenario configurations is illustrated, and tradeoff and guidelines for a cost optimal deployment of C-RAN are provided. The analysis of results for the real scenario of the city of Lisbon and its surrounding areas shows that the delay budget in the fronthaul network highly impacts on capital expenditures as well as on operational ones. It is shown that a larger delay budget enables an annual cost reduction up to 72% in urban areas and 54% in rural ones.

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Resilient BBU placement in 5G C-RAN over optical aggregation networks

Cited by 16 publications

References 20 publications

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Joint Optimization of Survivability and Energy Efficiency in 5G C-RAN With mm-Wave Based RRH

Cost-effective joint Optimisation of BBU Placement and Fronthaul Deployment in Brown-field Scenarios

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