Leveraging synergy of SDWN and multi‐layer resource management for 5G networks

Derakhshani, Mahsa; Parsaeefard, Saeedeh; Le‐Ngoc, Tho; Leon-Garcia, Alberto

doi:10.1049/iet-net.2017.0004

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…5G networks are envisioned to provide flexible edge-to-core infrastructure to offer diverse applications. The convergence of software-defined networking (SDN) and network virtualization (NV) is a promising approach to support such dynamic networks [1]. To satisfy customers' demands in terms of good quality of service (QoS) is very challenging to service providers (SPs) for such versatile traffics.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reservation and Deep Reinforcement Learning Based Autonomous Resource Slicing for Virtualized Radio Access Networks

et al. 2019

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The elastic reconstruction of 5G network services is expected to provide the capability of network slice orchestration to access the network on demand, guarantee service experience on demand, and construct services on demand as well as to construct basic network services with lower costs. It is challenging to have different applications served independently with a proper resource allocation mechanism according to their own requirements. In this paper, we propose a dynamic resource reservation and deep reinforcement learning-based autonomous virtual resource slicing framework for the next generation radio access network. The infrastructure provider periodically reserves the unused resource to the virtual networks based on their ratio of minimum resource requirements. Then, the virtual networks autonomously control their resource amount using deep reinforcement learning based on the average quality of service utility and resource utilization of users. With the defined framework in this paper, virtual operators can customize their own utility function and objective function based on their own requirements. The simulation results show the performances on convergence rate, resource utilization, and satisfaction of the virtual networks. INDEX TERMS Resource slicing, network virtualization, deep reinforcement learning.

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Section: Introductionmentioning

confidence: 99%

Dynamic Reservation and Deep Reinforcement Learning Based Autonomous Resource Slicing for Virtualized Radio Access Networks

et al. 2019

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“…In recent years, researches on synchronization and control for interdependent networks or “networks of networks” have been started 29‐37 . Reference 29 explored the synchronization of networks consisting of many subnetworks by using pinning control technology.…”

Section: Introductionmentioning

confidence: 99%

“…Reference 36 paid close attention to the interdependency between a communication network and a power grid network, and developed a novel effort broadcast algorithm to handle crash failures in the communication network that may cause cascading failures. For the current popular 5G network, based on separating the control and data layers, Reference 37 put forward a flexible 5G‐SDWN protocol structure, which can handle all the required functionalities in a more crosslayer manner.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time optimal pinning control and synchronization for partially interdependent networks

Wang

Guo

2020

Optim Control Appl Methods

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Summary For partially interdependent networks composed of two subnetworks, the finite‐time optimal pinning control problem is investigated. Among them, only a part of the nodes between the two subnetworks are interdependent on each other. In the network, the coupling relationship between any two nodes of the network is a continuous nonlinear function. Based on the pinning control, the optimal control theory, Kalman's controllability rank conditions, and introducing the Lagrange function and applying controllers to the partial nodes of the network, we propose some characterization indicators which ensure that the two subnetworks of partially interdependent networks can synchronize to the equilibrium points of their isolate systems, separately. Finally, some numerical simulations are performed on the partially interdependent networks including two small world subnetworks. The results show that the optimal pinning control method proposed in this article can greatly reduce the control costs and achieve the ideal synchronous status quickly.

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“…One of the basic concepts and key enablers to design 5G, in the most efficient and cost effective manner, is to resort to the concept of software defined networking and virtualization where 5G can be sliced between different services with divers quality of service (QoS) requirements [1]. In this so called networking slicing approach, providing isolation between slices in order to hold the QoS of each services under any users' variations of other users is of essential which calls for highly efficient resource management in this context [2].…”

Section: Introductionmentioning

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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Leveraging synergy of SDWN and multi‐layer resource management for 5G networks

Cited by 14 publications

References 40 publications

Dynamic Reservation and Deep Reinforcement Learning Based Autonomous Resource Slicing for Virtualized Radio Access Networks

Dynamic Reservation and Deep Reinforcement Learning Based Autonomous Resource Slicing for Virtualized Radio Access Networks

Finite‐time optimal pinning control and synchronization for partially interdependent networks

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

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