M.M.Zonoozi and P. Dassanayake, "User Mobility Modeling and Characterization of Mobility Patterns", IEEE Journal of Sel. Areas in Communications,Vol. 15, No. 7, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Hong and Rappaport proposed a dwell time distribution dependent on the speed of the mobile and the radius of the cell. In [16] and [17] sum of hyperexponential distributions (SOHYP) was used to model the dwell time. Lin et al. used general distributions as cell residence time distributions. In [19] and [20] the authors shown that the cell residence time is properly described by generalized gamma distribution. In this paper we allow the dwell time to have arbitrary distribution, or the knowledge of the residence time distribution is not necessary at all, rather measurement data on this time ....

....a PH with phases by the method presented in [24] The original distribution was the generalized gamma distribution, with pdf: 0 1 1 3254 6 . 0 25487:9 7: 8 A where 6 . is the gamma function defined as 6 . B C ED 2F7:9 7 GIH D . In [19] and [20] the authors have shown that the dwell time can be properly modeled by this distribution. Figure 1 and 2 show the distribution functions and the densities of the generalized gamma distribution and the fitted PH distribution. The results prove that the fitted PH approximates the original ....

Zonoozi, M.; Dassanayake, P. User Mobility Modeling and Characterization of Mobility Patterns. IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, Sep. 1997, pp. 1239-1252

....pattern and or of the initial placement on the node spatial distribution that results after a large number of mobility steps must be precisely evaluated. Probabilistic mobility modeling has been extensively studied in the related eld of cellular networks. For example, Zonoozi and Dassanayake [28] have shown (by means of extensive simulations and goodnessof t tests) that the cell residence time of a mobile user moving according to a given mobility pattern follows a generalized gamma distribution. In the Brownian mobility model, it has been shown that, given the user s location at time ....

M.M. Zonoozi, P. Dassanayake, \User Mobility Modeling and Characterization of Mobility Patterns", IEEE Journal of Selected Areas in Comm., Vol. 15, n. 7, pp. 1239-1252, 1997. 16

....conclusions regarding critical network parameters can be provided. However, the accuracy of the results heavily depends on how close the chosen model is to the real scenario. Mobility modeling has been extensively studied in the eld of cellular networks. For example, Zonoozi and Dassanayake [20] have shown (by means of extensive simulations and goodness of t tests) that the cell residence time of a mobile user moving according to a given mobility pattern follows a generalized gamma distribution. In the Brownian mobility model, it has been shown that, given the user s location at time ....

M.M. Zonoozi, P. Dassanayake, \User Mobility Modeling and Characterization of Mobility Patterns", IEEE Journal of Selected Areas in Comm., Vol. 15, n. 7, pp.

....when it enters the cell. The residual dwell time of a mobile that initiates new call is different, since it sets up the connection somewhere inside the cell. Authors dealing with mobility models therefore often define two different dwell time distributions for handover and for new calls (e.g. [18] and [19] and references therein) Here we suppose that the residual dwell times are given by distribution, or by statistical data. The session length begins when the mobile starts transmitting (somewhere in the network, not necessarily in the examined cell) and ends when the mobile terminates ....

....Two connection types were present in the system: voice calls with constant 32 kbps rate and multimedia connections. The latter had 128 kbps minimum, 256 kbps average and 1024 kbps peak transmission rate. The dwell time of the customers was modeled with generalized gamma distribution, according to [18] and [19] Phase type distributions with six phases were fitted to the dwell time distributions with the method described in [23] The first scenario was examined by the approximate method and computer simulations as well. Figure 2 and Figure 3 shows the blocking probabilities of handover and new ....

Zonoozi, M.; Dassanayake, P. User Mobility Modeling and Characterization of Mobility Patterns. IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, Sep. 1997, pp. 1239-1252

....of a user to visit cells in the adjacent region. Details about such location prediction schemes can be found in [3] 4] 6] A significant amount of research has also been done is computing the probabilistic models about the arrival time and residence time probability distribution [19] [20], 21] The general approach tries to collect statistics of multiple users in the region and fit the information into known probabilistic model. The assumption in using a probability distribution to predict users movement is that such distribution follows a stationary stochastic process. Since ....

M. M. Zonoozi and P. Dassanayake, "User Mobility Modeling and Characterization of Mobility Patterns," IEEE Journal on Selected Areas in Communication, 15(7), pp. 1239-1252, September 1997.

....that, channel holding time for a connection in a cell depends on the unencumbered cell residence time (i.e. cell residence time if the connection is of an infinite duration) and the remaining connection duration. In practice, unencumbered cell residence time may not be exponentially distributed [16], in which case, the strategy proposed in [15] will not be theoretically valid. Also, as in [11] Yu and Leung s model assumes that all connection requests are identical, which is not valid if multimedia services are to be supported by the wireless network. In contrast, in this paper, we consider ....

M. M. Zonoozi and P. Dassanayake. User mobility modeling and characterization of mobility patterns. IEEE Journal on Selected Areas in Communications, 15:1239--1252, September 1997.

.... crossings (1 3) the user speed is sampled from a Gaussian distribution with mean v, variance # 2 v and truncated in the range [v min ; v max ] pedestrian users can move without limitations inside the whole scenario according to a random walk model, which is a derivation of that proposed in [10]. In our random walk model, the users trajectories and speeds are randomly defined at call set up, and can be changed during the call at instants (denoted as nodes) that are randomly chosen according to a negative exponential distribution, having mean T change , describing the time interval ....

M. M. Zonoozi, P. Dassanayake, "User Mobility Modeling and Characterization of Mobility Patterns", Journal on Selected Areas in Communications,

....the terminal follows the path specified by that action. The time needed to cross each cell in the identified path (i.e. cell residence time) is provided by the Handover Time Generator Object implementing the Generalised Gamma Distribution which is considered the best fit for cell residence times [5]. Each time the user moves from one cell to another, the Mobility Function Object is notified and all needed information (e.g. time, cell history) is passed to it. IV. RMPG APPLICATION IN PATH PREDICTION The implemented platform was used to evaluate a variant of the path prediction algorithm ....

M.Zonoozi, and P.Dassanayake, "User Mobility Modeling and Characterization of Mobility Patterns", IEEE JSAC, Vol.15, No.7, 1997.

....[0, #max ] allows performing the analysis in only one of these two intervals. 0 200 400 600 800 1000 1200 0 0.005 0.01 0.015 z (m) f Z (z) d=100m d=200m Fig. 3. pdf of traveled distance. R = 1 Km. b) Distribution of #: The distribution we employed for r.v. # is based on biased sampling [6]: f # (#) sin(#) 2 if 0 # and 0 otherwise. In addition, truncation is applied so that # [# min , # max ] By making this assumption, we intend to exclude any path that enters the HA and then goes back to the inner source cell. c) Distribution of V : The speed of the MT is considered to ....

....assumption, we intend to exclude any path that enters the HA and then goes back to the inner source cell. c) Distribution of V : The speed of the MT is considered to be modeled by a Gaussian distribution (actually, a truncated Gaussian to avoid negative speeds) A similar assumption is made in [6, 10] and [19] Since this is the speed distribution for all MTs, the speed of an MT entering the HA is described by the biased sampling distribution f V # (v) vf V (v) E[V ] see [23] Figure 3 shows the distribution f Z (z) for different values of d and a cell radius R = 1 Km. It is worth ....

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M. M. Zonoozi and P. Dassanayake, "User mobility modeling and characterization of mobility patterns," IEEE Journal on Selected Areas in Communications, vol. 15, pp. 1239--1252, Sept. 1997.

....of the suggested architecture in light of path prediction misses and specific time scheduling for stochastic relocation. Specifically, we simulated the movement of a mobile user for 3 h (approximately 190 handovers) The distribution assumed for cell residence times is the one suggested in Ref. [16], namely, the Generalized Gamma distribution [26] with probability density function G: Gt; a; b; c c b ac Ga t ac21 e 2t=b t; a; b; c . 0 3 In Eq. 3) G( denotes the Gamma function. The assumed cell radius was 50 m, while the average speed of the mobile terminal was 10 km h (close ....

M. Zonoozi, P. Dassanayake, User mobility modeling and characterization of mobility patterns, IEEE Journal on Selected Areas in Communications 15 (7) (1997).

....and is given by: F IH J K L 1 9 : 2 M 4 ON 87:P8 A 2H (3) Note that the load is measured in Erlang. 3. 3 Mobility Model Several mobility models, such as the random walk model and the fluid flow model are often used to depict MS moving behavior in simulations and analyses [10, 11, 12]. In our simulation, we consider a more realistic mobility model. When a new call is generated, the MS initially chooses a speed which is uniformly distributed over [ Q , QTSVU ] and a moving direction which is uniformly distributed over W YX N 9Z Z[V . In Table 1. Parameter Values in ....

M. M. Zonoozi and P. Dassanayake; "User Mobility Modeling and Characterization of Mobility Patterns" IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, Sept. 1997.

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M.M.Zonoozi and P. Dassanayake, "User Mobility Modeling and Characterization of Mobility Patterns", IEEE Journal of Sel. Areas in Communications,Vol. 15, No. 7, 1997.

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M. M. Zonoozi and P. Dassanayake, "User mobility modeling and characterization of mobility patterns," IEEE J. Select. Areas Commun., vol. 15, p1239-1252, Sept. 1997.

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M. M. Zonoozi and P. Dassanayake, "User mobility modeling and characterization of mobility patterns," IEEE Journal on Selected Areas in Communications, vol. 15, no. 7, pp. 1239--1252, Sept. 1997.

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M. Zonoozi and P. Dassanayake. User Mobility Modeling and Characterization of Mobility Patterns. IEEE Journal on Selected Areas in Communications, 15(7):1239 to 1252, 1997. 16

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M. Zonoozi and P. Dassanayake. User mobility modeling and characterization of mobility patterns. IEEE Trans. on Selected Areas in Communications, pp. 1239--1252, Sept. 1997.

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M. M. Zonoozi and P. Dassanayake, \User mobility modeling and characterization of mobility patterns," IEEE Journal on Sel. Areas in Communications, vol. 15, pp. 1239-1252, Sept. 1997. 32

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M. M. Zonoozi and P. Dassanayake, "User mobility modeling and characterization of mobility patterns," IEEE J. Select. Areas Commun., vol. 15, pp. 1239--1252, Sept. 1997.

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M. Zonoozi and P. Dassanayake. User mobility modeling and characterization of mobility patterns. IEEE Trans. on Selected Areas in Communications, pp. 1239--1252, Sept. 1997. 31

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M. M. Zonoozi and P. Dassanayake. User mobility modeling and characterization of mobility patterns. IEEE Journal on Selected Areas in Communications, 15(7):1239-1252, September 1997

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M.M. Zonoozi and P. Dassanayake, "User mobility modeling and characterization of mobility patterns", IEEE JSAC, Vol. 15, n. 7 pp 1239-1252, September 1997.

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M.M. Zonoozi, and P. Dassanayake, "User Mobility Modeling and Characterization of Mobility Patterns," IEEE J. Selected Areas of Communications, vol. 15, pp 1239 -- 1252, Sept. 1997.

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M. M. Zonoozi and P. Dassanayake. User mobility modeling and characterization of mobility patterns. IEEE Journal on Selected Areas in Communications, pp 1239-1252, Sep. 1997

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M. M. Zonoozi and D. Dassanayake. User mobility modeling and characterization of mobility patterns. IEEE J. on Selected Areas in Comm., 15(7):1239-52, Sept. 1997. 25

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M. M. Zonoozi and P. Dassanayake, "User mobility modeling and characterization of mobility patterns," IEEE Journal on Selected Areas in Communications, vol. 15, pp. 1239--1252, Sept. 1997.

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