Users in cells: A data traffic analysis

Laner, Markus; Svoboda, Philipp; Schwarz, Štefan; Rupp, Markus

doi:10.1109/wcnc.2012.6214330

Cited by 40 publications

(26 citation statements)

References 19 publications

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“…Leveraging a well-known statistical model of 3G HSPA networks [10], one can infer the spectrum utilization pattern at each BS as a ratio of the traffic and the peak capacity of the BS. Given that the N BSs are independent and identically distributed in space and in spectrum utilization,the pdf of the power density (W/Hz) harvested from all N RF transmitters is the linear combinations of random variables.…”

Section: ) Realistic Traffic Load (Variable Spectrum Utilization)mentioning

confidence: 99%

Simultaneous Information and Energy Flow for IoT Relay Systems with Crowd Harvesting

et al. 2016

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Abstract-It is expected that the number of wireless devices will grow rapidly over the next few years due to the growing proliferation of Internet-of-Things (IoT). In order to improve the energy efficiency of information transfer between small devices, we review state-of-the-art research in simultaneous wireless energy and information transfer, especially for relay based IoT systems. In particular, we analyze simultaneous informationand-energy transfer from the source node, and the design of time-switching and power-splitting operation modes, as well as the associated optimization algorithms. We also investigate the potential of crowd energy harvesting from transmission nodes that belong to multiple radio networks. The combination of source and crowd energy harvesting can greatly reduce the use of battery power and increase the availability and reliability for relaying. We provide insight into the fundamental limits of crowd energy harvesting reliability based on a case study using real city data. Furthermore, we examine the optimization of transmissions in crowd harvesting, especially with the use of node collaboration while guaranteeing Quality-of-Service (QoS).

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Section: ) Realistic Traffic Load (Variable Spectrum Utilization)mentioning

confidence: 99%

Simultaneous Information and Energy Flow for IoT Relay Systems with Crowd Harvesting

et al. 2016

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“…The spatial distribution of users is captured using a HPPP Π U with mean λ U (t) at an arbitrary time instant t, this follows from the observations made in [20]. In [20] authors, demonstrated that the active downlink users are distributed according to PPP.…”

Section: B Spatio Temporal Model For Musmentioning

confidence: 99%

“…These parameters depend on the type of the day, i.e., weekend or weekday etc. The sinusoidal variation of mean density has been derived from actual cellular network in [19] and [20]. In this study, we assume operation on a typical weekday by selecting the values ofκ 5 = κ 6 = 0.5 and φ = −1.…”

Section: B Spatio Temporal Model For Musmentioning

confidence: 99%

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Energy harvesting empowered cognitive metro-cellular networks

Zaidi

Ghogho

McLernon

et al. 2014

2014 1st International Workshop on Cognitive Cellular Systems (CCS)

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Abstract-Harvesting energy from natural (solar, wind, vibration etc.) and synthesized (microwave power transfer) sources is envisioned as a key enabler for realizing green wireless networks. Energy efficient scheduling is one of the prime objectives of cognitive radio platforms. To that end, in this article, we present a comprehensive analytical framework to characterize the performance of a cognitive metro-cellular network empowered by the solar energy harvesting. The proposed model considers both spatial and temporal dynamics of the energy field and the mobile user traffic. Channel uncertainties are also captured in terms of a large scale path-loss and a small-scale Rayleigh fading. A new metric called 'energy outage probability' which characterizes the self-sustainable operation of the base stations under energy harvesting is proposed and quantified. It is shown that the energy outage probability is strongly coupled with the path-loss exponent, required quality-of-service, base station and user density. Moreover, the energy outage probability varies both on a daily and yearly basis depending on the solar geoemtry. It is shown that even in winter time BS can run 10-15 hours without any purchase of energy from the power grid.

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“…Addressing this challenge requires a concrete understanding of data hotspots in today's networks. While previous measurement studies have examined patterns of network-wide traffic [2,3,4,5,6,7,8], none have studied the impact of data hotspots in detail.…”

Section: Introductionmentioning

confidence: 99%

Understanding data hotspots in cellular networks

Nika

Ismail

Zhao

et al. 2014

10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

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The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today's cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to characterize in detail traffic hotspots in today's cellular networks using real data.

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