End-to-End Data Analytics Framework for 5G Architecture

Data analytics can be seen as a powerful tool for the fifth-generation (5G) communication system to enable the transformation of the envisioned challenging 5G features into a reality. In the current 5G architecture, some first features toward this direction have been adopted by introducing new functions in core and management domains that can either run analytics on collected communication-related data or can enhance the already supported network functions with statistics collection and prediction capabilities. However, possible further enhancements on 5G architecture may be required, which strongly depend on the requirements as set by vertical customers and the network capabilities as offered by the operator. In addition, the architecture needs to be flexible in order to deal with network changes and service adaptations as requested by verticals. This paper explicitly describes the requirements for deploying data analytics in a 5G system and subsequently presents the current status of standardization activities. The main contribution of this paper is the investigation and design of an integrated data analytics framework as a key enabling technology for the service-based architectures (SBAs). This framework introduces new functional entities for application-level, data network, and access-related analytics to be integrated into the already existing analytics functionalities and examines their interactions in a service-oriented manner. Finally, to demonstrate predictive radio resource management, we showcase a particular implementation for application and radio access network analytics, based on a novel database for collecting and analyzing radio measurements

Big Data Caching for Networking: Moving from Cloud to Edge

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings and $13$ Gbyte of storage size ($78\%$ of total library size), proactive caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special Issue on Communications, Caching, and Computing for Content-Centric Mobile Network

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin