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 5G networks with edge/cloud computing and exploitation of big data analytics can yield significant gains to mobile operators. In this article, proactive content caching in 5G 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.
Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.
We study online sequential regression with nonlinearity and time varying statistical distribution when the regressors lie in a high dimensional space. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional regressor space onto the underlying manifold as the modified regressor vectors for modeling of the nonlinear system. By using the proposed algorithm, we reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. The proposed techniques are specifically applicable to high dimensional streaming data analysis in a time varying environment. We demonstrate the significant performance gains in terms of mean square error over the other state of the art techniques through simulated as well as real data.
Localization of mobile users in indoor environments has many practical applications in daily life. In this paper, we study the performance of the received signal strength (RSS)-based radio frequency (RF) fingerprinting localization method in a shopping mall environment considering both calibration and practical measurement cases. In the calibration case, the test data for the RSS fingerprinting database are built offline by receiving signals from Global System for Mobile Communications (GSM) base stations, which are collected by a dedicated measurement tool, i.e. the Test Mobile System. In order to see the localization performance, the k-nearest neighbors (K-NN) and random decision forest (RDF) algorithms are implemented. The RDF algorithm provides a better localization performance than K-NN in this case. For the practical implementations, the RSS values of both GSM and Wi-Fi signals are collected by ordinary smartphones.Localization is performed using different classification algorithms, i.e. BayesNet, support vector machines, K-NN, RDF, and J48. Moreover, the effects of the received signal type, phone type, and number of reference points on localization performance are investigated.
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