Data-Driven Network Slicing From Core to RAN for 5G Broadcasting Services

Yang, Hui; Yu, Ao; Zhang, Jie; Nan, Jingwen; Bao, Bowen; Yao, Qiuyan; Cheriet, Mohamed

doi:10.1109/tbc.2020.3031742

Cited by 59 publications

(17 citation statements)

References 30 publications

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“…The authors of [70] focus on intelligent connection management with the aim to optimize user load balancing using deep reinforcement learning and graph neural networks techniques. In [71], the authors focus on orchestrated slicing of network resources in 5G RAN and core network. Finally, the authors of [72] propose a network outage-oriented model of virtualized O-RAN nodes in an O-cloud deployment.…”

Section: Research Activitiesmentioning

confidence: 99%

Open RAN—Radio Access Network Evolution, Benefits and Market Trends

2022

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Open RAN (radio access network) movement is perceived as a game changer, having robust potential to introduce shifts in mobile radio access networks towards tailor-made solutions based on the architecture decomposition. It is widely assumed that those changes will affect the approach to network deployments and supply chains of network elements and their further integration and maintenance. First deployments of O-RAN-based networks have already delivered broadband services to end users. In parallel, many proof-of-concept feature evaluations and theoretical studies are being conducted by academia and the industry. In this review, the authors describe the RAN evolution towards open models and make an attempt to indicate potential open RAN benefits and market trends.

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Section: Research Activitiesmentioning

confidence: 99%

Open RAN—Radio Access Network Evolution, Benefits and Market Trends

2022

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“…With the rapid development of AI technology, many related machine learning algorithms are applied for optical network optimization (Yang et al, 2020c;Yang et al 2021). There are several main categories of AI applications in edge cloud optical networks, including traffic prediction, resource assignment, and failure recovery.…”

Section: Related Workmentioning

confidence: 99%

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

et al. 2021

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With the rapid development of optical network and edge computing, the operation efficiency of the edge optical network has become more and more important, requiring an intelligent approach to enhance the network performance. To enhance the intelligence of the edge optical network, this article firstly provides the demand for the development of edge optical networks. Then, a cross-scene, cross-spectrum, and cross-service (3-CS) architecture for edge optical networks is presented. Finally, a federated transfer learning (FTL) framework, realizing a distributed intelligence edge optical network, is proposed. The usability of the proposed framework is verified by simulation.

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“…To tackle the high computational complexity caused by the large number of variables in the slice reconfiguration problem, a heuristic depth-first-search algorithm is proposed in [23] to find a set of possible reconfigurations, and a reinforcement learning approach is used to explore the multi-dimensional discrete action space. An end-to-end network slicing method for 5G networks without imposing constraints on available resources is proposed in [24], where the slice status is monitored and necessarily reconfigured in an online fashion via a heuristic approach. However, reconfiguring end-to-end slices based on instantaneous demands significantly increases the reconfiguration costs.…”

Section: A Related Workmentioning

confidence: 99%

Data-Driven Adaptive Network Slicing for Multi-Tenant Networks

Reyhanian,

Luo

2021

Preprint

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Network slicing to support multi-tenancy plays a key role in improving the performance of 5G networks. In this paper, we propose a two time-scale framework for the reservationbased network slicing in the backhaul and Radio Access Network (RAN). In the proposed two time-scale scheme, a subset of network slices is activated via a novel sparse optimization framework in the long time-scale with the goal of maximizing the expected utilities of tenants while in the short time-scale the activated slices are reconfigured according to the time-varying user traffic and channel states. Specifically, using the statistics from users and channels and also considering the expected utility from serving users of a slice and the reconfiguration cost, we formulate a sparse optimization problem to update the configuration of a slice resources such that the maximum isolation of reserved resources is enforced. The formulated optimization problems for long and short time-scales are non-convex and difficult to solve. We use the ℓq-norm, 0 < q < 1, and group LASSO regularizations to iteratively find convex approximations of the optimization problems. We propose a Frank-Wolfe algorithm to iteratively solve approximated problems in long time-scales. To cope with the dynamical nature of traffic variations, we propose a fast, distributed algorithm to solve the approximated optimization problems in short time-scales. Simulation results demonstrate the performance of our approaches relative to optimal solutions and the existing state of the art method.

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Data-Driven Network Slicing From Core to RAN for 5G Broadcasting Services

Cited by 59 publications

References 30 publications

Open RAN—Radio Access Network Evolution, Benefits and Market Trends

Open RAN—Radio Access Network Evolution, Benefits and Market Trends

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

Data-Driven Adaptive Network Slicing for Multi-Tenant Networks

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