Model-based movement patterns play a crucial role in evaluating the performance of mobility-dependent Personal Communication Service (PCS) strategies. This study proposes a new normal walk model to represent more closely the daily movement patterns of a mobile station (MS) in PCS networks than a conventional random walk model. A drift angle θ in this model is applied to determine the relative direction in which an MS handoffs in the next one step, based on the concepts that most real trips follow the shortest path and the directions of daily motion are mostly symmetric. Hence, θ is assumed to approach the normal distribution with the parameters: µ is set to 0and σ is in the range of 5to 90. Varying σ thus redistributes the probabilities associated with θ to make the normal mobility patterns more realistic than the random ones. Experimental results verify that the proposed normal walk is correct and valid for modeling an n-layer mesh cluster of PCS networks. Moreover, when σ = 79.5, a normal walk can almost represent, and even replace, a random walk.

In this paper, we consider a location management scheme using Limited Pointer forwarding from Commonly visited sites (LPC) strategy for Personal Communication Services (PCS) networks. The Commonly Visited Site (CVS) is defined as a site in which a mobile user is found with high probability. A feature of the strategy is that it skips updating location information of the mobile user, provided that the mobile user moves within its CVSs. Such a strategy is expected to significantly reduce the location update cost. We evaluate the location management cost of the LPC scheme by employing a Continuous-Time Markov Chain (CTMC) model. We show that the LPC scheme can reduce the location management cost of a highly mobile user who is found in its CVS with high probability.