5G is expected to be commercially rolled out by 2020 and targets challenging KPI requirements. Due to the increased complexity of the mobile networks, meeting the requirements by the existing manual network operation techniques is infeasible for 5G. Also, the manual daily network operations escalate mobile networks Operation Expenses (OPEX). Without a dramatic increase in OPEX, dealing with the network complexity and meeting the KPIs for each challenging use case is only possible by the realization of autonomous network operation functions. The autonomous networks' components and the intelligent functions are developed with the cutting edge Machine Learning (ML) and Artificial Intelligence (AI) models. Furthermore, to be able to realize the efficient and scalable network automation capabilities, ONAP (Open Network Automation Platform) and OSM (Open Source Management and orchestration) frameworks are proposed for 5G. These frameworks provide a set of plug and play automation functions as well as APIs, which enables the mobile operators to extend the framework capabilities by developing their network automation functions to tackle their individual business and operational challenges. The automated network management and orchestration function, in turn, enables the operators to keep the OPEX to reasonable levels and meet the challenging KPI requirements of 5G. This article as a baseline provides an overview of 4G Long‐Term Evolution (LTE) Self‐Organizing Networks (SON) and presents 5G automation concepts and frameworks. Furthermore, in this article, the prominent 5G automation frameworks namely ONAP and OSM are compared, and evidently, the ONAP framework has enriched network automation functions and stands out as the choice of the majority of the telecommunication industry. Finally, this article summarizes the cutting edges AI models and provides a literature review on AI applications in the scope of mobile network automation.
Drive testing continues to play a key role in mobile network optimization for operators but its high cost is a big concern. Alternative approaches like virtual drive testing (VDT) target device testing in the lab whereas MDT or crowdsourcing based approaches are limited by the incentives users have to participate and contribute measurements. With the aim of augmenting drive testing and significantly reducing its cost, we propose GenDT, a novel deep generative model that synthesizes high-fidelity time series of key radio network key performance indicators (KPIs). The training of GenDT relies on a relatively small amount of real-world measurement data along with corresponding and easily accessible network and environment context data. Through this, GenDT learns the relationship between context and radio network KPIs as they vary over time, and therefore trained GenDT model can subsequently be relied on to generate time series for different KPIs for new drive test routes (trajectories) without having to collect field measurements. GenDT represents an initial attempt at enabling efficient drive testing via generative modeling. Evaluations with real-world mobile network drive testing measurement datasets from two countries demonstrate that GenDT can synthesize significantly more dependable data than a range of baselines. We further show that GenDT has the potential to significantly reduce the drive testing related measurement effort, and that GenDT-generated data yields similar results to that with real data in the context of two downstream use cases -QoE prediction and handover analysis. CCS CONCEPTS• Networks → Mobile networks; • Computing methodologies → Neural networks.
Service-level mobile traffic data enables research studies and innovative applications with a potential to shape future service-oriented communication systems and beyond. However, real-world datasets reporting measurements at the individual service level are hard to access as such data is deemed commercially sensitive by operators. APPSHOT is a model for generating synthetic high-fidelity city-scale snapshots of service level mobile traffic. It can operate in any geographical region and relies solely on easily available spatial context information such as population density, thus allowing the generation of new and open traffic datasets for the research community. The design of APPSHOT is informed by an original characterization of servicelevel mobile traffic data. APPSHOT is a novel conditional GAN design instantiated by a convolutional neural network generator and two discriminators. The model features several other innovative mechanisms including multi-channel and overlapping patch based generation to address the unique challenges involved in generating mobile service traffic snapshots. Experiments with ground-truth data collected by a major European operator in multiple metropolitan areas show that APPSHOT can produce realistic network loads at the service level for areas where it has no prior traffic knowledge, and that such data can reliably support service-oriented networking studies.
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