Probabilistic model for mobility in cellular network subscriber (original) (raw)

Subscriber Mobility Modeling in Wireless Networks

2007

In this paper, a simplified model for user mobility behavior for wireless networks is developed. The model takes into account speed and direction of motion, the two major characteristics of user mobility. A user mobility simulation based on the model is developed, and its results are compared with those in published work. The transient performance metrics of a mobile network are analyzed in terms of trajectory prediction, mean location update rate, and new/handoff call residence time distribution. The proposed mobility model yields results that match very well with those obtained from previously reported complex mobility models.

Characterizing and modeling user mobility in a cellular data network

Proceedings of the 2nd ACM international workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks - PE-WASUN '05, 2005

The demand for cellular data networks is expected to increase with 3G and beyond technologies accompanied by highbandwidth consumer services, such as wireless video and camera phones. User mobility affects quality of service, and makes capacity planning more difficult. This paper presents an analysis of user mobility patterns based on data traffic traces from a major regional CDMA2000 cellular network. We find low overall mobility in the network, power-law characteristics in user mobility profiles, and weak correlations between call activity and mobility levels for individual users. We also find that users concentrate their activity in a "home cell" with frequent shorter trips to other locations in the network. Based on the empirical findings, we develop and parameterize a model of cellular data user mobility and show its practical use in simulation.

User mobility modeling and characterization of mobility patterns

IEEE Journal on Selected Areas in Communications, 1997

A mathematical formulation is developed for systematic tracking of the random movement of a mobile station in a cellular environment. It incorporates mobility parameters under the most generalized conditions, so that the model can be tailored to be applicable in most cellular environments. This mobility model is used to characterize different mobility-related traffic parameters in cellular systems. These include the distribution of the cell residence time of both new and handover calls, channel holding time, and the average number of handovers. It is shown that the cell resistance time can be described by the generalized gamma distribution. It is also shown that the negative exponential distribution is a good approximation for describing the channel holding time.

How a new realistic mobility model can affect the relative performance of a mobile networking scheme

The validity of the mobility model used to evaluate a cellular network determines the validity of the evaluation. In the literature, unrealistic assumptions on mobility are exercised for the sake of simplicity. In this paper, we present a novel mobility model which is realistic in the sense that it captures the moving-in-groups, conscious traveling and inertial behaviours of the subscribers while respecting the non-pass-through feature of structures like households and preserving the autonomy of the subscribers. The mobility and call patterns of the subscribers are determined according to the locus of the subscriber over a real map. Thus, our model allows the subscribers to leave home or arrive home, walk or drive in the streets, get on the highways at specific entry points together with numerous hot and blind spots in the terrain, like city centers and lakes. The call pattern of a subscriber is affected by the type of structure he is in. The model can work on real maps to simulate the mobility patterns in real life.We have evaluated the proposed model against the well-known way point mobility model.We also analyzed the effect of the mobility model on systems with and without guard channels.

Mobility modeling of rush hour traffic for location area design in cellular networks

Proceedings of the 3rd ACM international workshop on Wireless mobile multimedia, 2000

User mobility is one of the most important factors affecting the network performance in a cellular network. Increased mobility results in more location updates, handovers and hence an increase in the number of messages exchanged between various entities in the system. This signaling not only puts extra load on the radio interface but also on the infrastructure equipment. Thus, it is required to minimize the rate of occurrence of these mobility related events by optimally dividing the system into areas which have a minimum exchange of traffic. This paper presents a mobility model which can be used to predict the transient behavior of the traffic on the routes connecting these areas in a given network during rush hours when the capability of the network to handle mobility related signaling traffic is put to a severe test. Some algorithms which can use this information to optimally plan the network are also considered.

Application of a realistic mobility model to call admissions in DS-CDMA cellular systems

2001

In this paper, we present a novel mobility model which is realistic in the sense that it captures the moving-ingroups, conscious traveling and inertial behaviors of the subscribers while respecting the non-pass-through feature of some structures like households, and preserving the autonomy of the subscribers. The mobility and call patterns of the subscribers are determined according to the locus of the subscriber over a real map. The model can work on real maps to simulate the mobility patterns in real life. We have evaluated the proposed model against the well-known way point mobility model.

Traffic Modeling of Mobile Cellular Network Using Combination of Limited and Unlimited User Groups

In a mobile cellular network there are two user groups: users inside the cell responsible for newly originating call and users enter the cell from surrounding cells responsible for handover call. In this paper we assume that the number of users inside a microcell is far larger than the handover users therefore the PCT-I (Pure Chance Traffic I) traffic i.e. traffic of unlimited user is applicable for newly originating call and PCT-II i.e. traffic of limited user is suitable for handover traffic. A two dimensional Markov chain of combination of PCT-I and PCT-II traffic (including reserved channels for handover traffic) is proposed hence solved in a different method called tabular solution. The profile of 'call blocking probability of shared users' and 'probability of FT (forced termination)' is shown against offered traffic and number of channels taking 'number of users' and 'reserved channels' as parameters. The impact of handover users, number of reserved and shared channel on the performance of a network are analyzed explicitly along with algorithm of traffic solution.

Traffic Modeling in Mobile Communication Networks

International Journal of Computer Applications, 2012

This paper is focused on traffic modeling in Mobile Communication networks. This research is aimed at developing a traffic models that will predict a blocking probability for voice calls and handover calls blocking probability in mobile communication networks (GSM). The high number of block calls experience in mobile network, especially during the Busy Hour (BH) has leads to poor Quality of Service (QOS) delivering in mobile network. These block calls experience in mobile network should be reduced to a certain low values (2% value in line with NCC recommended standard), to ensure good QOS. The developed traffic models are focused on new voice calls within the cell, handover calls in and out of a cell. The developed traffic models are designed based on the numbers of channels, partition into two segments in a cell network. The cell technology is homogenous in nature; therefore it is applicable to the entire mobile communication system. The analytical method is deployed, and the collection of traffic data with equipment know as the Operation and Maintenance Center (OMC-counter) which is in built in the mobile communication network is used. The OMC-counter runs on Linux operation software, which helps to capture the number of arrival calls and service time in a specified interval. The arrival rate is assumed to be Poisson and the service time is also, assumed to be exponentially distributed and independence identical distributed. These parameters are used in the development of the traffic models. The developed traffic models are blocking probability for voice calls and handover blocking probability for handover calls. These developed (propose) traffic models are validated using MATLAB (version 7.6.3. 325) program and compared with the conventional Erlang B for accuracy. These traffic models are used to manage the block calls experience in the mobile network. Also used in a balance relationship between cost incurred in mobile communication by operators and service render to the mobile communication subscribers.

Mobility modeling in third-generation mobile telecommunications systems

IEEE Personal Communications, 1997

In mobile communications, mobility modeling is involved in several aspects related to signaling and traffic load analysis. In third generation systems, the influence of mobility on the network performance (e.g., handover rate) will be strengthened, mainly due to the huge number of mobile users in conjunction with the small cell size. In particular, the accuracy of mobility models becomes essential for the evaluation of system design alternatives and network implementation cost issues. In this paper, we propose three basic types of mobility models, which are appropriate for the analysis of the full range of mobile communications' design issues. The models provide different levels of detail regarding the user mobility behavior. In particular: (a) the City Area Model traces user motion at an area zone level, (b) the Area Zone Model considers users moving on a street network and (c) the Street Unit Model tracks user motion with an accuracy of a few meters. The validity of the basic models for mobile communications' design aspects is highlighted. Moreover, an "integrated mobility modeling tool", which considers the basic mobility models as components is proposed, aiming at the development of a refined modeling approach. This is achieved by improving the accuracy of the input parameters of each basic model, via the exchange of some specific (mobility related) parameters among the component models. To justify the applicability of the proposed integrated tool for both the analysis of design aspects and network planning, indicative results are presented, derived from simulation-based application examples of the three basic mobility models.

Random waypoint mobility model in cellular networks

Wireless Networks, 2006

In this paper we study the so-called random waypoint (RWP) mobility model in the context of cellular networks. In the RWP model the nodes, i.e., mobile users, move along a zigzag path consisting of straight legs from one waypoint to the next. Each waypoint is assumed to be drawn from the uniform distribution over the given convex domain. In this paper we characterise the key performance measures, mean handover rate and mean sojourn time from the point of view of an arbitrary cell, as well as the mean handover rate in the network. To this end, we present an exact analytical formula for the mean arrival rate across an arbitrary curve. This result together with the pdf of the node location, allows us to compute all other interesting measures. The results are illustrated by several numerical examples. For instance, as a straightforward application of these results one can easily adjust the model parameters in a simulation so that the scenario matches well with, e.g., the measured sojourn times in a cell.