Simulating population dependent PCS network models using time warp

Authors:
Christopher D. Carothers;Yi-Bing Lin;Richard M. Fujimoto
Affiliations:
College of Computing, Georgia Institute of Technology, Atlanta, Georgia;Department of Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.;College of Computing, Georgia Institute of Technology, Atlanta, Georgia
Venue:
WSC '95 Proceedings of the 27th conference on Winter simulation
Year:
1995

Citing 7
Cited 2

Virtual time

ACM Transactions on Programming Languages and Systems (TOPLAS)
Distributed discrete-event simulation

ACM Computing Surveys (CSUR)
Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem

Communications of the ACM
Parallel discrete event simulation

Communications of the ACM - Special issue on simulation
State of the art in parallel simulation

WSC '92 Proceedings of the 24th conference on Winter simulation
A case study in simulating PCS networks using Time Warp

PADS '95 Proceedings of the ninth workshop on Parallel and distributed simulation
Distributed Simulation of Large-Scale PCS Networks

MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems

Rapid simulation of wireless systems

PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
SWiMNet: a scalable parallel simulation testbed for wireless and mobile networks

Wireless Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The demand for mobile communications has led to intensive research and development efforts for complex PCS (personal communication service) networks. Capacity planning and performance modeling is necessary to maintain a high quality of service to the mobile subscriber while minimizing cost to the PCS provider. A question of pragmatic interest concerns the modeling of subscriber or portable movement in a PCS network. Typically, portable movement models are based on a probabilistic distribution function with fixed mean. These models may be an over simplification of real portable movement patterns. For example, portable movements during rush hour traffic may be slowed due to traffic jams. However, when the volume of vehicular traffic is less, portables are allowed to move more freely and with greater speed. To capture this type of portable movement behavior, we develop a population dependent PCS model where portable movement is based on the number of portables currently residing in that service area or cell. Because of the large amount of computation required to simulate PCS networks, we implement this model on a distributed Time Warp simulator, which has been shown to reduce the execution time of a single run from 20 hours down to 3.5 hours. Using this simulation model, we study the effect of different call workloads and population dependent portable movement patterns on PCS blocking statistics and present our preliminary results.