The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Djamil Aissani - One of the best experts on this subject based on the ideXlab platform.

  • Bounds of the stationary distribution in M/G/1 retrial queue with two way communication and n types of outgoing calls ation and n type of outgoing call
    Yugoslav Journal of Operations Research, 2019
    Co-Authors: Lala Alem Maghnia, Mohamed Boualem, Djamil Aissani
    Abstract:

    In this article we analyze the M/G/1 retrial queue with two way communication and n type of outgoing calls from a stochastic comparison viewpoint. The main idea  is that given a complex Markov Chain  which cannot be analyzed numerically, we propose to bound it by a new Markov Chain which is easier to solve by using a stochastic comparison approach. Particularly, we analyze the notion of monotonicity of the transition operator of the Embedded Markov Chain relative to the stochastic and convex orderings. Bounds are also obtained for the stationary distribution of the Embedded Markov Chain at departures epochs. Additionally,  the performance measures of the  system considered can be estimated by those of the  M/M/1 retrial queue with two way communication  when the service time distribution is NBUE (respectively, NWUE). Finally, we test numerically the accuracy of the proposed bounds.

  • strong stability of the Embedded Markov Chain in an gi m 1 queue with negative customers
    Applied Mathematical Modelling, 2010
    Co-Authors: Karim Abbas, Djamil Aissani
    Abstract:

    Abstract This paper is devoted to the investigation for sufficient conditions of the strong stability of the Embedded Markov Chain in GI/M/1 queueing system with negative customers. After perturbing the occurrence rate of the negative customers, we prove the strong stability of the considered Markov Chain with respect to a convenient weight variation norm. Furthermore, we estimate the deviation of its transition operators and provide an upper bound to the approximation error. This results allow us to understand how the negative customers will affect the system’s level of performance.

Xian Wang - One of the best experts on this subject based on the ideXlab platform.

  • cost analysis of movement based location management in pcs networks an Embedded Markov Chain approach
    IEEE Transactions on Vehicular Technology, 2014
    Co-Authors: Xian Wang, Rose Qingyang Hu, Shijinn Horng
    Abstract:

    In this paper, we develop an approach of Embedded Markov Chain to analyze the signaling cost of a movement-based location management (MBLM) scheme. This approach distinguishes itself from those developed in the literature in the following aspects. 1) It considers the location area (LA) architecture used by personal communication service (PCS) networks for location management. 2) It considers two different call handling models that determine after a call whether a location update should be performed. 3) It considers the effect of the call holding time on the call handling models. 4) It proposes to use a fluid flow model to describe the dependence between the cell and the LA residence time. We derive closed-form analytical formulas for the signaling cost, whose accuracy is manifested by a simulation. Based on the analytical formulas, we conduct a numerical study to evaluate the influence of various parameters on the signaling cost. The formulas can contribute to the implementation of the MBLM scheme in PCS networks including Fourth-Generation (4G) Long-Term Evolution. The modeling approach developed in this paper can be exploited to model other location management schemes.

  • ICNC - Cost analysis of movement-based location update scheme using an approach of Embedded Markov Chain
    2014 International Conference on Computing Networking and Communications (ICNC), 2014
    Co-Authors: Xian Wang, Rose Qingyang Hu, Geng Wu
    Abstract:

    In this paper we develop an approach of Embedded Markov Chain to analyze the signaling cost of movement-based location update (MBLU) scheme implemented in a personal communication service (PCS) network with location area (LA) architecture. We propose to use a fluid flow model to characterize the dependency between cell residence time and LA residence time. We derive analytical formulas for the expected numbers of various types of location updates and for the expected paging area size. The accuracy of the analytical formulas is tested using a simulation. The analytical formulas can guide the implementation of the MBLU scheme in PCS networks. Moreover, the modeling approach can be explored to study other LU schemes.

  • Cost analysis of movement-based location update scheme using an approach of Embedded Markov Chain
    2014 International Conference on Computing Networking and Communications (ICNC), 2014
    Co-Authors: Xian Wang, Rose Qingyang Hu, Geng Wu
    Abstract:

    In this paper we develop an approach of Embedded Markov Chain to analyze the signaling cost of movement-based location update (MBLU) scheme implemented in a personal communication service (PCS) network with location area (LA) architecture. We propose to use a fluid flow model to characterize the dependency between cell residence time and LA residence time. We derive analytical formulas for the expected numbers of various types of location updates and for the expected paging area size. The accuracy of the analytical formulas is tested using a simulation. The analytical formulas can guide the implementation of the MBLU scheme in PCS networks. Moreover, the modeling approach can be explored to study other LU schemes.

Kanjian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • potentials based optimization with Embedded Markov Chain for stochastic constrained system
    Nonlinear Dynamics, 2012
    Co-Authors: Kang Cheng, Kanjian Zhang
    Abstract:

    In this paper, a RBF neural network based on-line optimization algorithm with performance potentials analysis method is presented for a class of stochastic constrained dynamic systems. The control signals of the considered systems are constrained to a range according to a subset of the whole state space. With the conception of an Embedded Markov Chain, an optimization approach on the basis of potentials is presented for a stochastic constrained system, where the optimization criterion is the long-time average performance. With this approach, the computation burden has been reduced because it only requires one to compute the control strategy on the states concerned, which are a subset of the whole state space. Furthermore, with the characteristic of approximation performance of RBF neural network, the potentials and the transition probability matrix are estimated conveniently by a sample path compared with the statistic approach or the method by solving the Poisson equation. The effectiveness of the optimization approach has been shown by the simulation results, finally.

Bjorn Bottcher - One of the best experts on this subject based on the ideXlab platform.

  • Embedded Markov Chain approximations in Skorokhod topologies
    Probability and Mathematical Statistics, 2019
    Co-Authors: Bjorn Bottcher
    Abstract:

    We prove a J1-tightness condition for Embedded Markov Chains and discuss four Skorokhod topologies in a unified manner. To approximate a continuous time stochastic process by discrete time Markov Chains, one has several options to embed the Markov Chains into continuous time processes. On the one hand, there is a Markov embedding which uses exponential waiting times. On the other hand, each Skorokhod topology naturally suggests a certain  embedding. These are the step function embedding for J1, the linear interpolation embedding forM1, the multistep embedding for J2 and a more general embedding for M2. We show that the convergence of the step function embedding in J1 implies the convergence of the other embeddings in the corresponding topologies. For the converse statement, a J1-tightness condition for Embedded time-homogeneous Markov Chains is given.Additionally, it is shown that J1 convergence is equivalent to the joint convergence in M1 and J2.

  • Embedded Markov Chain approximations in skorokhod topologies
    arXiv: Probability, 2014
    Co-Authors: Bjorn Bottcher
    Abstract:

    In order to approximate a continuous time stochastic process by discrete time Markov Chains one has several options to embed the Markov Chains into continuous time processes. On the one hand there is the Markov embedding, which uses exponential waiting times. On the other hand each Skorokhod topology naturally suggests a certain embedding. These are the step function embedding for $J_1$, the linear interpolation embedding for $M_1$, the multi step embedding for $J_2$ and a more general embedding for $M_2$. We show that the convergence of the step function embedding in $J_1$ implies the convergence of the other embeddings in the corresponding topologies, respectively. For the converse statement a $J_1$-tightness condition for Embedded Markov Chains is given. The result relies on various representations of the Skorokhod topologies. Additionally it is shown that $J_1$ convergence is equivalent to the joint convergence in $M_1$ and $J_2$.

Lothar Breuer - One of the best experts on this subject based on the ideXlab platform.