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Example of packet encapsulation in the LTE user plane.

Example of packet encapsulation in the LTE user plane.

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Article
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Traffic classification will be a key aspect in the operation of future 5G cellular networks, where services of very different nature will coexist. Unfortunately, data encryption makes this task very difficult. To overcome this issue, flow-based schemes have been proposed based on payload-independent features extracted from the Internet Protocol (IP...

Context in source publication

Context 1
... layer adds a header and passes the data to the next layer, until the lowest layer is reached, where actual communication occurs through the physical medium. 1 shows an example of the encapsulation scheme in the user plane of the LTE radio interface. The upper level is the application layer, which contains application-specific protocols (e.g., Hypertext Transfer Protocol -HTTP-, File Transfer Protocol -FTP-, etc.). ...

Citations

... However, supervised schemes require a labeled training dataset. Other alternatives use unsupervised learning algorithms to classify connections without the need of a previously-labeled dataset [38,39]. In [38], an unsupervised method for offline coarse-grained traffic classification in cellular radio access networks is presented. ...
... Other alternatives use unsupervised learning algorithms to classify connections without the need of a previously-labeled dataset [38,39]. In [38], an unsupervised method for offline coarse-grained traffic classification in cellular radio access networks is presented. This method relies on the fact that the identification of the class of service for a specific connection can be performed from a set of traffic descriptors showing the properties of data bursts in the connection. ...
... Unfortunately, radio connection traces do not explicitly register these traffic descriptors at the burst level, so that they must be estimated from other traffic parameters collected per connection. In the absence of labeled data that could be used as ground truth, the authors in [38] validate their method by comparing the traffic mix resulting from their classification algorithm against mobile traffic statistics published by a vendor. Results show that traffic shares per application class estimated by the proposed method are similar to those provided by a vendor report. ...
Article
Full-text available
In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.