The Experts below are selected from a list of 32829 Experts worldwide ranked by ideXlab platform

Carlos Sarraute - One of the best experts on this subject based on the ideXlab platform.

  • Forecasting Individual Demand in Cellular Networks
    2018
    Co-Authors: Guangshuo Chen, Sahar Hoteit, Marco Fiore, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    We leverage two large-scale real-world Datasets to provide the pioneer results on the limits of predictability of per-user Mobile Data Traffic demands over time and space. Using information theory tools, we measure the maximum predictability that any algorithm has potential to achieve. We first focus on the predictability of Mobile Data Traffic consumption patterns in isolation. Our results show that it is theoretically possible to anticipate individual demands with a typical accuracy of 85%. Then, we analyze the joint predictability of Mobile Data Traffic demands and mobility patterns. Their correlation that we find leads to a higher theoretical potential performance in joint prediction. Besides, we propose a novel practice to evaluate the spatiotemporal correlation of per-user Mobile Data Traffic.

  • Mobile Data Traffic Modeling: Revealing Temporal Facets
    2017
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    Using a large-scale Dataset collected from a major 3G network in a dense metropolitan area, this paper presents the first detailed measurement-driven model of Mobile Data Traffic usage of smartphone subscribers. Our main contribution is a synthetic, measurement-based, Mobile Data Traffic generator capable of simulating Traffic-related activity patterns for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., "when" and "how much" Traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original Dataset. Broadly, our observations bring important insights into network resource usage. We also discuss relevant issues in Traffic demands and describe implications in network planning and privacy.

  • Mobile Data Traffic Modeling: Revealing Temporal Facets
    Computer Networks, 2017
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    This paper presents a detailed measurement-driven model of Mobile Data Traffic usage of smartphone subscribers, using a large-scale Dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, Mobile Data Traffic generator capable of simulating Traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers' routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., " when " and " how much " Traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original Dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in Traffic demands and describe implications in network solution evaluation and privacy.

  • Mobile Data Traffic modeling
    Computer Networks, 2017
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    This paper presents a detailed measurement-driven model of Mobile Data Traffic usage of smartphone subscribers, using a large-scale Dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, Mobile Data Traffic generator capable of simulating Traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers routinary behavior, followed by a detailed investigation of subscribers temporal usage patterns (i.e., when and how much Traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently replicates a subscribers profiles for these two time periods when compared to the original Dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in Traffic demands and describe implications in network solution evaluation and privacy.

  • Measurement-driven Mobile Data Traffic modeling in a large metropolitan area
    2015
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    Understanding Mobile Data Traffic demands is crucial to the evaluation of strategies addressing the problem of high bandwidth usage and scalability of network resources, brought by the pervasive era. In this paper, we conduct the first detailed measurement-driven modeling of smartphone subscribers' Mobile Traffic usage in a metropolitan scenario. We use a large-scale Dataset collected inside the core of a major 3G network of Mexico's capital. We first analyse individual subscribers routinary behaviour and observe identical usage patterns on different days. This motivates us to choose one day for studying the subscribers' usage pattern (i.e., "when" and "how much" Traffic is generated) in detail. We then classify the subscribers in four distinct profiles according to their usage pattern. We finally model the usage pattern of these four subscriber profiles according to two different journey periods: peak and non-peak hours.We show that the synthetic trace generated by our Data Traffic model consistently imitates different subscriber profiles in two journey periods, when compared to the original Dataset.

Honggang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • characterizing and modeling social Mobile Data Traffic in cellular networks
    Vehicular Technology Conference, 2016
    Co-Authors: Zhifeng Zhao, Honggang Zhang
    Abstract:

    Understanding Traffic characteristics in cellular networks is of great significance for better network design and performance optimization. The rapid development of various social networking applications for smart devices makes it an imperative to carry out cellular Data Traffic analysis further into the application level. In this paper, based on a plenty of practical Mobile Data Traffic records, we focus on three typical application types and draw conclusions in terms of statistical characteristics and appropriate distribution model for social Mobile Data Traffic. Firstly, the universal existence of burstiness and self-similarity is demonstrated by testing Traffic series at different time scales. Afterwards, α-stable distributions are used to model Traffic series benefiting from their internal burstiness and self- similarity. The minor fitting errors verify the validity of α-stable model and a preliminary Traffic prediction shows the usefulness of α-stable model for further Traffic analysis.

  • VTC Spring - Characterizing and Modeling Social Mobile Data Traffic in Cellular Networks
    2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016
    Co-Authors: Zhifeng Zhao, Honggang Zhang
    Abstract:

    Understanding Traffic characteristics in cellular networks is of great significance for better network design and performance optimization. The rapid development of various social networking applications for smart devices makes it an imperative to carry out cellular Data Traffic analysis further into the application level. In this paper, based on a plenty of practical Mobile Data Traffic records, we focus on three typical application types and draw conclusions in terms of statistical characteristics and appropriate distribution model for social Mobile Data Traffic. Firstly, the universal existence of burstiness and self-similarity is demonstrated by testing Traffic series at different time scales. Afterwards, α-stable distributions are used to model Traffic series benefiting from their internal burstiness and self- similarity. The minor fitting errors verify the validity of α-stable model and a preliminary Traffic prediction shows the usefulness of α-stable model for further Traffic analysis.

Zhifeng Zhao - One of the best experts on this subject based on the ideXlab platform.

  • characterizing and modeling social Mobile Data Traffic in cellular networks
    Vehicular Technology Conference, 2016
    Co-Authors: Zhifeng Zhao, Honggang Zhang
    Abstract:

    Understanding Traffic characteristics in cellular networks is of great significance for better network design and performance optimization. The rapid development of various social networking applications for smart devices makes it an imperative to carry out cellular Data Traffic analysis further into the application level. In this paper, based on a plenty of practical Mobile Data Traffic records, we focus on three typical application types and draw conclusions in terms of statistical characteristics and appropriate distribution model for social Mobile Data Traffic. Firstly, the universal existence of burstiness and self-similarity is demonstrated by testing Traffic series at different time scales. Afterwards, α-stable distributions are used to model Traffic series benefiting from their internal burstiness and self- similarity. The minor fitting errors verify the validity of α-stable model and a preliminary Traffic prediction shows the usefulness of α-stable model for further Traffic analysis.

  • VTC Spring - Characterizing and Modeling Social Mobile Data Traffic in Cellular Networks
    2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016
    Co-Authors: Zhifeng Zhao, Honggang Zhang
    Abstract:

    Understanding Traffic characteristics in cellular networks is of great significance for better network design and performance optimization. The rapid development of various social networking applications for smart devices makes it an imperative to carry out cellular Data Traffic analysis further into the application level. In this paper, based on a plenty of practical Mobile Data Traffic records, we focus on three typical application types and draw conclusions in terms of statistical characteristics and appropriate distribution model for social Mobile Data Traffic. Firstly, the universal existence of burstiness and self-similarity is demonstrated by testing Traffic series at different time scales. Afterwards, α-stable distributions are used to model Traffic series benefiting from their internal burstiness and self- similarity. The minor fitting errors verify the validity of α-stable model and a preliminary Traffic prediction shows the usefulness of α-stable model for further Traffic analysis.

Kolar Purushothama Naveen - One of the best experts on this subject based on the ideXlab platform.

  • Mobile Data Traffic Modeling: Revealing Temporal Facets
    2017
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    Using a large-scale Dataset collected from a major 3G network in a dense metropolitan area, this paper presents the first detailed measurement-driven model of Mobile Data Traffic usage of smartphone subscribers. Our main contribution is a synthetic, measurement-based, Mobile Data Traffic generator capable of simulating Traffic-related activity patterns for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., "when" and "how much" Traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original Dataset. Broadly, our observations bring important insights into network resource usage. We also discuss relevant issues in Traffic demands and describe implications in network planning and privacy.

  • Mobile Data Traffic Modeling: Revealing Temporal Facets
    Computer Networks, 2017
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    This paper presents a detailed measurement-driven model of Mobile Data Traffic usage of smartphone subscribers, using a large-scale Dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, Mobile Data Traffic generator capable of simulating Traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers' routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., " when " and " how much " Traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original Dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in Traffic demands and describe implications in network solution evaluation and privacy.

  • Mobile Data Traffic modeling
    Computer Networks, 2017
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    This paper presents a detailed measurement-driven model of Mobile Data Traffic usage of smartphone subscribers, using a large-scale Dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, Mobile Data Traffic generator capable of simulating Traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers routinary behavior, followed by a detailed investigation of subscribers temporal usage patterns (i.e., when and how much Traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently replicates a subscribers profiles for these two time periods when compared to the original Dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in Traffic demands and describe implications in network solution evaluation and privacy.

  • Measurement-driven Mobile Data Traffic modeling in a large metropolitan area
    2015
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    Understanding Mobile Data Traffic demands is crucial to the evaluation of strategies addressing the problem of high bandwidth usage and scalability of network resources, brought by the pervasive era. In this paper, we conduct the first detailed measurement-driven modeling of smartphone subscribers' Mobile Traffic usage in a metropolitan scenario. We use a large-scale Dataset collected inside the core of a major 3G network of Mexico's capital. We first analyse individual subscribers routinary behaviour and observe identical usage patterns on different days. This motivates us to choose one day for studying the subscribers' usage pattern (i.e., "when" and "how much" Traffic is generated) in detail. We then classify the subscribers in four distinct profiles according to their usage pattern. We finally model the usage pattern of these four subscriber profiles according to two different journey periods: peak and non-peak hours.We show that the synthetic trace generated by our Data Traffic model consistently imitates different subscriber profiles in two journey periods, when compared to the original Dataset.

  • measurement driven Mobile Data Traffic modeling in a large metropolitan area
    IEEE International Conference on Pervasive Computing and Communications, 2015
    Co-Authors: Eduardo Mucelli Rezende Oliveira, Kolar Purushothama Naveen, Aline Carneiro Viana, Carlos Sarraute
    Abstract:

    Understanding Mobile Data Traffic demands is crucial to the evaluation of strategies addressing the problem of high bandwidth usage and scalability of network resources, brought by the pervasive era. In this paper, we conduct the first detailed measurement-driven modeling of smartphone subscribers' Mobile Traffic usage in a metropolitan scenario. We use a large-scale Dataset collected inside the core of a major 3G network of Mexico's capital. We first analyse individual subscribers routine behavior and observe identical usage patterns on different days. This motivates us to choose one day for studying the subscribers' usage pattern (i.e., “when” and “how much” Traffic is generated) in detail. We then classify the subscribers in four distinct profiles according to their usage pattern. We finally model the usage pattern of these four subscriber profiles according to two different journey periods: peak and non-peak hours. We show that the synthetic trace generated by our Data Traffic model consistently imitates different subscriber profiles in two journey periods, when compared to the original Dataset.

Michail Katsigiannis - One of the best experts on this subject based on the ideXlab platform.

  • A Demand Model for Mobile Data Traffic in the 5G Era: Case of Finland
    Advances in Human Resources Management and Organizational Development, 2018
    Co-Authors: Michail Katsigiannis
    Abstract:

    This chapter examines how to estimate and forecast the market demand of Mobile Data Traffic in the 5G era. The research objective is to develop a demand model for forecasting the market price and quantity of Traffic in the Finnish Mobile Data communications market for the period between 2016 and 2020. The market price of Traffic unit (GB/month), quantity, revenue, and profits are empirically estimated and forecast. The results show that the improvements of network performance, reflected by the user experienced Data rate, cause a drop in the price from 0.8 to 0.27 € per GB/month between 2016 and 2020 (tenfold Traffic growth). Also, a more than threefold increase is shown in Mobile Data revenues, whereas the profitability remains at a high level for the minimum marginal cost of 0.08 €.

  • Energy consumption of radio access networks in Finland
    Telecommunication Systems, 2014
    Co-Authors: Michail Katsigiannis, Heikki Hämmäinen
    Abstract:

    Energy consumption of radio access networks is related to the speed and volume of Mobile Data Traffic. Finland aims to achieve two conflicting targets by 2015, the one of 100 Mbps user Data rate provision to nearly all citizens and the other of the reduction of Information and Communications Technology industry carbon footprint. The purpose of this paper is to evaluate the energy consumption for several radio access networks deployment scenarios. Mobile operators can advance energy efficiency through (1) energy optimization of individual sites and (2) choice of energy efficient network deployment strategies. Models for the site power consumption, the average site Traffic load factor and the average subscription Mobile Data Traffic, are developed. The results show a reduction of energy consumption for Mobile network based on LTE radio access technology which operates at 1800 MHz in urban region and 800 MHz in suburban and rural regions. In the dense urban region the LTE base stations are installed on the existing sites and in the less-dense urban region, wide-to-local area offloading is required.

  • 5GU - Cost comparison of Licensed Shared Access (LSA) and MIMO scenarios for capacity growth in Finland
    Proceedings of the 1st International Conference on 5G for Ubiquitous Connectivity, 2014
    Co-Authors: Michail Katsigiannis, Arturo Basaure, Marja Matinmikko
    Abstract:

    The high Mobile Data Traffic growth requires investments in radio access networks. Network capacity expansion can be achieved by increasing the number of base stations, increasing the network spectral efficiency and obtaining additional spectrum. This study assumes a constant number of base station to compare two potential future network deployment scenarios; the Licensed Shared Access scenario (LSA), which adds spectrum to the network and a Multi-Input and Multi-Output (4×2 MIMO) antenna technology, which increases the network's spectral efficiency. Both deployment scenarios are studied for the urban regions in Finland. The purpose is to examine the spectrum availability and evaluate under which conditions LSA is more likely to be implemented in the Finnish market. With the assumptions taken, the results show that the LSA scenario provides more capacity but the MIMO scenario provides a more cost efficient alternative. The MIMO technology is preferable than a LSA deployment for Mobile Data Traffic growth rate less than 2.7. For larger growth rate (up to 3.5) the LSA scenario is a feasible solution either as independent or complementary technique.

  • Mobile Network Offloading: Deployment and Energy Aspects
    International Journal of Interdisciplinary Telecommunications and Networking, 2012
    Co-Authors: Michail Katsigiannis
    Abstract:

    The Mobile Data Traffic growth and the high fraction of indoor-generated Traffic push Mobile operators to devise new deployment strategies such as Mobile network offloading. The purpose of this paper is to evaluate the energy consumption and the deployment cost, based on the demanded Traffic level, for a joint macro-femtocell network which enables Mobile network offloading in Helsinki Metropolitan Area by 2015. This deployment is compared to an optimized only macro cellular network. The study tries to resolve under what conditions, in terms of demanded Traffic, deployment cost and energy consumption, a Mobile operator should deploy femtocells. Assuming that only the new network infrastructure is installed by 2015, the results show that wide-to-local area offloading is beneficial for a Mobile operator to handle the Mobile Data Traffic growth, reduce the deployment costs and the energy consumption of the radio access network.