452,475 research outputs found
Optimising 5G infrastructure markets: the business of network slicing
Proceeding of: IEEE Conference on Computer Communications, INFOCOM 2017, Atlanta, Georgia, USA, 1-4 May 2017In addition to providing substantial performance enhancements, future 5G networks will also change the mobile network ecosystem. Building on the network slicing concept, 5G allows to "slice" the network infrastructure into separate logical networks that may be operated independently and targeted at specific services. This opens the market to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, we need new algorithms for the allocation of network resources that consider these new players. In this paper, we address this issue by designing an algorithm for the admission and allocation of network slices requests that (i) maximises the infrastructure provider's revenue and (ii) ensures that the service guarantees provided to tenants are satisfied. Our key contributions include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modelled as a Semi-Markov Decision Process) and the optimisation of the infrastructure provider's revenue, and (iii) the design of an adaptive algorithm (based on Q-learning) that achieves close to optimal performance.This research work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA (Grant Agreement No. 671584). The work of A. Banchs was partially supported
by the Spanish Ministry of Economy and Competitiveness under the THWART project (Grant TEC2015-70836-ERC)
Road Roughness Estimation Using Machine Learning
Road roughness is a very important road condition for the infrastructure, as
the roughness affects both the safety and ride comfort of passengers. The roads
deteriorate over time which means the road roughness must be continuously
monitored in order to have an accurate understand of the condition of the road
infrastructure. In this paper, we propose a machine learning pipeline for road
roughness prediction using the vertical acceleration of the car and the car
speed. We compared well-known supervised machine learning models such as linear
regression, naive Bayes, k-nearest neighbor, random forest, support vector
machine, and the multi-layer perceptron neural network. The models are trained
on an optimally selected set of features computed in the temporal and
statistical domain. The results demonstrate that machine learning methods can
accurately predict road roughness, using the recordings of the cost
approachable in-vehicle sensors installed in conventional passenger cars. Our
findings demonstrate that the technology is well suited to meet future pavement
condition monitoring, by enabling continuous monitoring of a wide road network
A machine learning approach to 5G infrastructure market optimization
It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent "slices" of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of NEC Laboratories Europe was supported by the 5G-Transformer project (Grant Agreement No. 761536)
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