Impact of mobility models on the cell residence time in WLAN networks

Zola, Enrica; Barcelo-Arroyo, Francisco

doi:10.1109/sarnof.2009.4850370

Cited by 24 publications

(16 citation statements)

References 14 publications

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“…3(b). The segment residence time (T), during which the DMT remains at a segment, has a gamma distribution [7]. For a given T, the maximum moving speed (V) of the DMT crosscutting a segment is calculated as / V Diameter of a hexagonal segment T  .…”

Section: Simulated Power Consumption Resultsmentioning

confidence: 99%

Smart WLAN Discovery for Power Saving of Dual‐Mode Terminals

Park¹,

Lee²

2013

ETRI Journal

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Section: Simulated Power Consumption Resultsmentioning

confidence: 99%

Smart WLAN Discovery for Power Saving of Dual‐Mode Terminals

Park¹,

Lee²

2013

ETRI Journal

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“…That is, by generating flows that arrive and terminate, random idle times at flow terminations and random movement patterns for users. To model the residence time in the three regions more realistically, the lognormal distribution is chosen [27]. The duration of an idle period and the size of a flow are also considered to be lognormally distributed [28], [29].…”

Section: B Validation Of the Analytical Modelmentioning

confidence: 99%

Performance Analysis of Two-Tier Wireless Networks With Dynamic Traffic, Backhaul Constraints, and Terminal Mobility

Bernal-Mor

Pla

Martínez-Bauset

et al. 2016

IEEE Trans. Veh. Technol.

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Bernal Mor, E.; Pla, V.; Martínez Bauset, J.; Luis Guijarro (2016) Abstract-During the last years, mobile cellular networks have witnessed an enormous growth in the carried data-traffic volume. The current networks' features are not enough to cope with this traffic trend and the concept of small cells has emerged as a feasible solution to increase the network capacity. However, the deployment of small cells introduces several technical challenges such as the cross-tier interference between the macrocell and the small cells, or the use of the subscriber land-line to send the backhaul data. In this paper, an analytical model is proposed to study the impact that the user traffic dynamics, the mobility of macrocell users, the scheme chosen to associate macrocell users to the small cells and the changing available capacity of the small cells backhaul have on the system performance. To make the solution of the model computationally feasible, we exploit the time-scale decomposition approach. In most practical scenarios, the arrival and departure rates of traffic flows are much larger than the rate of events associated with the mobility of macrocell users. Then, flows perceive that macrocell users are still. This model is applied to identify the scheme to associate macrocell users to the small cells which maximizes the performance perceived by the small cell users.

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“…In (Zola & Barcelo-Arroyo, 2009), the authors analyse the time that an MS remains under the coverage of the same AP (cell residence time) in a WLAN of medium size designed for pedestrians. The study is carried out by running simulations for different AP layouts and mobility models with Omnet++ (OMNET++).…”

Section: Mobility Patternsmentioning

confidence: 99%