Intelligent paging in wireless networks: random mobility models and grouping algorithms for locating subscribers

The impact of random user motion on intelligent paging schemes is examined and quantified. To this end, a straight-line motion pattern and two Markov chains are presented to model the random movement of mobile users (MUs) within the location area (LA). Results show that the cost of intelligent paging in the two cases of Markovian motion is more expensive than that of a simple straight-line motion. Also, two kinds of algorithms for grouping the paging areas (PAs) are considered. It is shown that grouping based on geographical location of PAs (rhombus grouping) is the most obvious one, whereas grouping based on probability of locating MUs (high-probability grouping) is more cost-effective.