A 3W network strategy for mobile data traffic offloading

Choi, Yong Chan; Ji, Hyon Wook; Park, Jae-yoon; Kim, Hyunchul; Silvester, J.A.

doi:10.1109/mcom.2011.6035825

Cited by 34 publications

(9 citation statements)

References 1 publication

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“…However, such interworking architectures rely on the evolution of mobile access networks with redesigned protocols. Based on off-the-shelf radio access technologies, there have been several proposed WiFi offloading frameworks in the literature [11,7,14,15,16].…”

Section: Offloading Frameworkmentioning

confidence: 99%

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Vehicular WiFi offloading: Challenges and solutions

Cheng

Liu

Zhang

et al. 2014

Vehicular Communications

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WiFi offloading is envisioned as a promising solution to the mobile data explosion problem in cellular networks. WiFi offloading for moving vehicles, however, poses unique characteristics and challenges, due to high mobility, fluctuating mobile channels, etc. In this paper, we focus on the problem of WiFi offloading in vehicular communication environments. Specifically, we discuss the challenges and identify the research issues related to drive-thru Internet access and effectiveness of vehicular WiFi offloading. Moreover, we review the state-of-the-art offloading solutions, in which advanced vehicular communications can be employed. We also shed some lights on the path for future research on this topic.

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Section: Offloading Frameworkmentioning

confidence: 99%

“…An architecture, named 3W, is introduced in [7], where 3W represents WCDMA, WIBRO (Korean name for the IEEE 802.16e mobile WiMAX), and WiFi. 3W provides a heterogenous coverage over South Korea: 3G WCDMA network provides nationwide coverage, while WIBRO covers 84 cities, and WiFi covers indoor and street areas.…”

Section: Offloading Frameworkmentioning

confidence: 99%

Vehicular WiFi offloading: Challenges and solutions

Cheng

Liu

Zhang

et al. 2014

Vehicular Communications

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“…The system parameter values, unless otherwise specified, Figures 3 and 4 show the evolution of the handover probability, both P h and P h 1 , with µ r,e . Curves labeled with 'Exp' correspond to analytic results considering exponentially distributed cell residence times and obtained using (6). Curves labeled with 'Logn P h ' and 'Logn P h 1 ' are obtained considering exponentially distributed cell residence times to determine (α, S), and then using lognormal distributions and their residual distributions to model cell residence times when computing P h and P h 1 from (4), as explained in Section III.…”

Section: Numerical Evaluationmentioning

confidence: 99%

“…Also, the handover probability was studied in [1]. However, despite the enormous increase in the volume and economical relevance of mobile data traffic caused by the introduction of smartphones [6], to the best of our knowledge, such type of studies have not been carried out for elastic flows so far. Modeling the elastic flow duration and handover related metrics is qualitatively different and more complex than modeling their streaming traffic counterparts, as the duration of an elastic flow is heavily dependent on the network load.…”

Section: Introductionmentioning

confidence: 99%

Handover Performance for Elastic Flows in Mobile Cellular Networks

2012

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Abstract-The characterization of flows duration and their handover metrics is of paramount importance for the design and resource management of mobile networks. Elastic flows duration characterization is more involved than for streaming sessions, as it depends on the network load. In this work, the distribution of flows duration is characterized by a phase type distribution. The model is then applied to determine the handover probability under different cell residence time and flow size distributions. Simulation results confirm that the proposed model yields very accurate results under realistic assumptions.Index Terms-Handover probability, elastic traffic, elastic flow duration.

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“…In [1], Cisco estimates that mobile video traffic will attain at a CAGR of 54% per year for the period 2016-2021 and account for more than 78% of mobile data traffic in 2021. Unfortunately, this massive surge in mobile video traffic has caused an unprecedented degree of pressure on the limited capacity of cellular networks, and ultimately degrades the user-perceived video quality [2]. The provision of support for the explosive traffic growth in mobile networks is quite challenging.…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

Park

Song

2018

IEEE Trans. on Mobile Comput.

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Abstract-This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the ε-Nash equilibrium.Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.

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A 3W network strategy for mobile data traffic offloading

Cited by 34 publications

References 1 publication

Vehicular WiFi offloading: Challenges and solutions

Vehicular WiFi offloading: Challenges and solutions

Handover Performance for Elastic Flows in Mobile Cellular Networks

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

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