Energy-Efficient Robust Resource Provisioning in Virtualized Wireless Networks

Jumba, Vikas; Parsaeefard, Saeedeh; Derakhshani, Mahsa; Le‐Ngoc, Tho

doi:10.1109/icuwb.2015.7324447

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…In the worst-case approach, the considered value of channel coefficients have to belong to the related uncertainty sets, and also they have to provides the minimum rate on each codebook for the assigned considered user. Here, the worstcase approach is transformed into the protection function which simplifies the robust problem considerably [29], [41].…”

Section: Worst Case Resource Allocation Problemmentioning

confidence: 99%

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

Moltafet

Parsaeefard

Javan

et al. 2019

IEEE Trans. Veh. Technol.

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In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this regard, by supposing multiple input and single output (MISO) transmission technology, a novel robust radio resource allocation is proposed where considering uncertain channel state information (CSI), the worst case approach is applied. The main goal of the proposed radio resource allocation is to, maximize the system sum rate with maximum available power at radio remote head (RRH), minimum rate requirement of each slice, maximum frounthaul capacity of each RRH, user association, and SCMA constraints. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where in each iteration, beamforming and joint codebook allocation and user association subproblem are solved separately. By introducing some auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm (MMA) is applied. Via numerical results, the performance of the proposed system model versus different system parameters and for different channel models are investigated. Index Terms-5G, Sparse code multiple access (SCMA), C-RAN, robust resource allocation.

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Section: Worst Case Resource Allocation Problemmentioning

confidence: 99%

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

Moltafet

Parsaeefard

Javan

et al. 2019

IEEE Trans. Veh. Technol.

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“…Effective network slicing management and prioritization in 5G systems provides higher QoS in individual slices and increases utilization of network resources. Vikas Jumba proposed an optimal algorithm for resource provisioning to simultaneously satisfy the rate and resource requirements of slices in [11]. Resource provisioning means making network resources available to be utilized by users.…”

Section: Related Workmentioning

confidence: 99%

“…To demonstrate the ability of admission control algorithm to efficiently adjust the minimum slice requirements based on priority, the authors in [12] presented a joint resource provisioning and admission control policy in virtualized wireless networks. The same authors proposed a robust resource provisioning policy in [13] to maximize the EE of virtualized networks while satisfying QoS of slices.…”

Section: Related Workmentioning

confidence: 99%

“…These parameters are; the number of sc-BSs J, the number of slices K, the number of UEs I, the set of edges connecting UEs and sc-BSs E and the user demand of one user . In step 2, we schedule slices based on the predefined priority in equations (10) and (11) and update available radio resources and user proportion in each scheduled period. Then we build a bi-partite graph.…”

Section: Definition 2: Demand Bi-partite Graphmentioning

confidence: 99%

See 1 more Smart Citation

A Framework of Resource Provisioning and Customized Energy-Efficiency Optimization in Virtualized Small Cell Networks

Addo¹,

Boateng²,

Jiang³

2018

KSII TIIS

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The continuous increase in the cost of energy production and concerns for environmental sustainability are leading research communities, governments and industries to amass efforts to reduce energy consumption and global CO 2 footprint. Players in the information and communication industry are keen on reducing the operational expenditures (OpEx) and maintaining the profitability of cellular networks. Meanwhile, network virtualization has been proposed in this regard as the main enabler for 5G mobile cellular networks. In this paper, we propose a generic framework of slice resource provisioning and customized physical resource allocation for energy-efficiency and quality of service optimization. In resource slicing, we consider user demand and population resources provisioning scheme aiming to satisfy quality of service (QoS). In customized physical resource allocation, we formulate this problem with an integer non-linear programming model, which is solved by a heuristic algorithm based on minimum vertex coverage. The proposed algorithm is compared with the existing approaches, without consideration of slice resource constraints via system-level simulations. From the perspective of infrastructure providers, traffic is scheduled over a limited number of active small-cell base stations (sc-BSs) that significantly reduce the system energy consumption and improve the system's spectral efficiency. From the perspective of virtual network operators and mobile users, the proposed approach can guarantee QoS for mobile users and improve user satisfaction.

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“…In order to cope with uncertain input parameters, RO [10] is applied to solve optimization problems. Indeed, RO has been successfully employed in different contexts, such as in resource allocation in OFDM networks [26] with uncertainty in channel state information, in cloud resource provisioning without perfect knowledge of demands and price [21], and even in the virtual network embedding problem on a physical network substrate in recent works [27][28][29][30] that usually deal with uncertain service demands.…”

Section: Related Workmentioning

confidence: 99%

A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

Blanco

Taboada

Fajardo

et al. 2017

Mobile Information Systems

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In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.

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Energy-Efficient Robust Resource Provisioning in Virtualized Wireless Networks

Cited by 9 publications

References 23 publications

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

A Framework of Resource Provisioning and Customized Energy-Efficiency Optimization in Virtualized Small Cell Networks

A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

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