Applied Numerical Methods for Engineers Using MATLAB
Applied Numerical Methods for Engineers Using MATLAB
The Möbius Framework and Its Implementation
IEEE Transactions on Software Engineering
TOMSPIN-a Tool for Modelling with Stochastic Petri Nets
PNPM '95 Proceedings of the Sixth International Workshop on Petri Nets and Performance Models
A Call Admission Control Scheme for Heterogeneous Service Considering Fairness in Wireless Networks
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
Multi-services MAC protocol for wireless networks
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Validation of a Load Shared Integrated Network with Heterogeneous Services
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
IEEE Transactions on Wireless Communications
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Existing Markov modeling tools include high level Petri nets and process algebras, or low level statistical packages with programmed or form matrices. However, we and other modelers often describe our work in Markov Chain state space format. With the rising popularity of graphical tools, a graphical Markov Chain state space modeler makes sense to simplify development of these models. However, as networks become large and complex, the graphical tool must implement large state spaces quickly, by allowing the modeler to specify patterns for state and transition generation. Our graphic multidimensional Markov Chain modeler implements the Gauss-Seidel solution. It enables modelers to design state spaces graphically, eliminating programming and associated debugging. This tool can be used by programmers and non-programmers alike to speed up research efforts of all types, and can also be used as a training tool for probability or simulation courses teaching modeling. As an example application, we analyze a case study involving Common Radio Resource Management (CRRM) applied to load shared wireless networks.