Autonomous Network Slicing Prototype Using Machine-Learning-Based Forecasting for Radio Resources

2021 ◽  
Vol 59 (6) ◽  
pp. 73-79
Author(s):  
Nazih Salhab ◽  
Rami Langar ◽  
Rana Rahim ◽  
Sylvain Cherrier ◽  
Abdelkader Outtagarts
Keyword(s):  
Machine Learning ◽  
Network Slicing ◽  
Radio Resources ◽  
Autonomous Network

scholarly journals Performance Model for Video Service in 5G Networks

2020 ◽  
Vol 12 (6) ◽  
pp. 99
Author(s):  
Jiao Wang ◽  
Jay Weitzen ◽  
Oguz Bayat ◽  
Volkan Sevindik ◽  
Mingzhe Li
Keyword(s):  
Machine Learning ◽  
Bandwidth Allocation ◽  
Access Network ◽  
Service Level Agreement ◽  
Service Level ◽  
Performance Model ◽  
Radio Resource ◽  
Network Slicing ◽  
Optimal Bandwidth Allocation ◽  
Management Approaches

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

scholarly journals Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

2021 ◽  
Author(s):  
Luca Lusvarghi ◽  
Maria Luisa Merani
Keyword(s):  
Machine Learning ◽  
Resource Assignment ◽  
Radio Resource ◽  
Vehicle To Vehicle ◽  
Generation Times ◽  
Radio Resources ◽  
V2v Communications ◽  
Message Broadcasting ◽  
Cooperative Awareness Messages ◽  
Cooperative Awareness

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Optimal 5G network slicing using machine learning and deep learning concepts

2021 ◽  
Vol 76 ◽  
pp. 103518
Author(s):  
Mustufa Haider Abidi ◽  
Hisham Alkhalefah ◽  
Khaja Moiduddin ◽  
Mamoun Alazab ◽  
Muneer Khan Mohammed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Network Slicing ◽  
5G Network

scholarly journals Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
K. Koutlia ◽  
R. Ferrús ◽  
E. Coronado ◽  
R. Riggio ◽  
F. Casadevall ◽  
...  
Keyword(s):  
Software Defined Radio ◽  
Radio Channel ◽  
Radio Resource Management ◽  
Access Network ◽  
Network Slicing ◽  
Radio Access Network ◽  
Management Framework ◽  
Radio Access ◽  
Radio Resources ◽  
5G Systems

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.

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