A novel normal walk model for PCS networks with mesh cell configuration

Abstract

Model‐based movement patterns play a crucial role in evaluating the performance of mobility‐dependent Personal Communication Service (PCS) strategies, such as location updating and paging schemes. In this study, we propose a novel normal walk model to represent the daily mobility patterns of a mobile station (MS) in mesh PCS networks more closely than does a conventional random walk model. The proposed model applies a drift angle, θ, to determine the next relative direction in which an MS will leave a mesh cell in one step, based on the concepts that most real trips follow the shortest path and the directions of daily motion are mostly symmetric. Thus, the probability of θ is assumed to approach a normal distribution with the parameters: μ is set to 0° and σ falls in the interval [5°, 90°]. Varying σcan redistribute the probabilities of θ to make the movement patterns more realistic to represent the mobility of users in PCS networks. An analytical normal walk model is further formulated in a 6‐layer cluster with mesh cells for quantitative analysis. Experimental results demonstrate that for σ = 10°, 30°, 60°, and 90°, the discrepancies between the analytical computations (expected steps) and the simulated values (average steps) are all within ±0.55%, and most are within ±0.35%. Moreover, when σ is set to 79.5°, a normal walk can almost represent and even replace a random walk.