On the characteristics and origins of internet flow rates
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HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications
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Radio Resource Management Strategies in UMTS
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Energy consumption in mobile phones: a measurement study and implications for network applications
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Where's that phone?: geolocating IP addresses on 3G networks
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Distributed uplink scheduling in CDMA networks
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TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation
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Predicting handoffs in 3G networks
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IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
Power consumption analysis of constant bit rate video transmission over 3G networks
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RadioJockey: mining program execution to optimize cellular radio usage
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Empowering developers to estimate app energy consumption
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Screen-off traffic characterization and optimization in 3G/4G networks
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Characterize energy impact of concurrent network-intensive applications on mobile platforms
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Energy analysis and prediction for applications on smartphones
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Large-scale measurement and characterization of cellular machine-to-machine traffic
IEEE/ACM Transactions on Networking (TON)
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3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.