Location-Dependent Parameterization of a Random Direction Mobility Model

Gloss, Bernd; Scharf, Michael P.; Neubauer, David

doi:10.1109/vetecs.2006.1682998

Cited by 9 publications

(7 citation statements)

References 11 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To represent the user mobility, a random walk model [10] deployed in Manhattan-style street structure is considered, the path is composed by some lines and columns, so we can simplify the possible user movement into four directions (up,down,left,right) with the same movement probability. Three MCSs modes are selected for simulation with the different effective range QPSK1/2 -1000m, 16QAM1/2-800m, 64QAM3/4 -500m.…”

Section: Simulation Resultsmentioning

confidence: 99%

Link Adaptation for Video Multicasting in Mobile WiMAX Network

Chen¹,

Liao²,

Shi³

et al. 2009

2009 6th IEEE Consumer Communications and Networking Conference

View full text Add to dashboard Cite

IEEE802.16e based mobile WiMAX network provides a promising technology for broadband wireless access. Among all types of services supported by WiMAX, real time video multicast has attracted much attention. However, because of the absence of feedback channel for multicast service, it is challenging to provide link adaptation and resource allocation under the situation with diverse receiver distribution. The legacy method -using the most robust modulation mode for multicasting service, or select a leader as the representative -can not accurately reflect the variety of channel condition. In this paper, we firstly analyze the overhead problem caused by the periodic downlink channel quality report, then propose a light-weighted terminal triggered feedback mechanism to reduce the signaling overhead. Furthermore, with the combined consideration of layered video coding and adaptive modulation and coding, a utility-based resource allocation approach is proposed. The utility function is relating with the reception satisfaction and spectrum efficiency. The scheduling algorithm dynamically adjusts layer number and its bandwidth according to receiver distribution. Simulation results show the proposed feedback mechanism can greatly reduce the signaling overhead while maintain the accurate channel condition report. The scheduling algorithm can not only improve the link adaptation capability following the receivers' expectation and program popularity, but also outperform the fixed allocation method in term of system utility.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Link Adaptation for Video Multicasting in Mobile WiMAX Network

Chen¹,

Liao²,

Shi³

et al. 2009

2009 6th IEEE Consumer Communications and Networking Conference

View full text Add to dashboard Cite

show abstract

“…The Random Direction mobility model [1,22,23], sometimes called Random Walk, is a prominent alternative to the RWP model. It is just as easily implemented and even has a much simpler user distribution.…”

Section: Random Direction Mobility Modelmentioning

confidence: 99%

“…It uses linear programming, like in our previous work [26], and expresses the user distribution as equality constraints while minimising the flow of users turning around at intersections. It is similar to Reference [23] where parameters of the basic RD model are changed to fit statistics of the RWP model or measured traces.…”

Section: Equalising the User Distributionmentioning

confidence: 99%

Random direction or random waypoint? A comparison of mobility models for urban environments

Kraaier¹,

Killat²

2007

Trans Emerging Tel Tech

View full text Add to dashboard Cite

SUMMARYThe Random Direction (RD) and the Random Waypoint (RWP) mobility models have been discussed extensively in the literature. The focus of this discussion was the stationary distribution of users and speeds of users. In this paper, we extend the investigation to street-based variants of the two mobility models. We compare statistical properties including user distribution, sojourn times in hotspots and link durations in ad hoc networks. In order to motivate the use of street-based mobility models, we also perform a comparison with the basic models. Finally, we present our approach to achieve identical user distributions in both streetbased models and investigate the remaining differences, which prove to be negligible for wireless network simulations.

show abstract

“…The aim of [10] is similar to our own as the Random Direction mobility model is modified to adjust its stationary state. The approach, however, is different and not as suitable for vehicular mobility modeling because the model's parameters are adapted in areas rather than at discrete locations.…”

Section: Introductionmentioning

confidence: 99%

Controlling the Stationary Distribution of Mobile Users in Wireless Network Simulations

Kraaier

Killat

2007

2007 IEEE 66th Vehicular Technology Conference

View full text Add to dashboard Cite

In order to be able to simulate wireless networks in a non-uniform environment resulting in specific user distributions, it is essential to predict and control the stationary user distribution of the employed mobility model. We propose a framework to calculate turning probabilities at intersections of a graph-based mobility model to realize an arbitrary stationary user distribution on a street network. In this way, the user distribution can be fitted to a measured distribution. Our framework is able to cope with different average velocities on streets as well as different pausing probabilities at intersections. We evaluate its scope with the help of an example distribution on a European city center map featuring different street types and a variety of different crossings.

show abstract

Location-Dependent Parameterization of a Random Direction Mobility Model

Cited by 9 publications

References 11 publications

Link Adaptation for Video Multicasting in Mobile WiMAX Network

Link Adaptation for Video Multicasting in Mobile WiMAX Network

Random direction or random waypoint? A comparison of mobility models for urban environments

Controlling the Stationary Distribution of Mobile Users in Wireless Network Simulations

Contact Info

Product

Resources

About