Simulating population dependent PCS network models using Time Warp

Abstract

ABSTR.ACTThe demand for mobile communications over the past few years has grown at a rapid pace. This demand has led to intensive research and development efforts for complex PCS (personal communication service) networks. Capacity planning and pdrformance 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.

Causal order in parallel and distributed simulation systems

“…The approach of DES has been adopted for analytic purpose and virtual environment (VE) for many years. Over the past two decades, the rapid expansion of Internet and increasing accessibility to high performance parallel computer has made parallel and Analytic PADS has been successful in the areas of telecommunications [22,32,79], digital logic circuits [15,72,74,77], computer architectures [39,75,76] and transportation systems [147]. It has proven to be a cost-and time-effective way for developing new systems, particularly those that involve a high capital investment, and in testing design changes or policy changes to existing systems.…”