The complexity of the mobility tracking problem in a cellular environment has been characterized under an information-theoretic framework. Shannon's entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user's path updates (as opposed to the widely used location updates), the proposed adaptive on-line algorithm can learn subscribers' profiles. This technique evolves out of the concepts of lossless compression. The compressibility of the variable-to-fixed length encoding of the acclaimed Lempel--Ziv family of algorithms reduces the update cost, whereas their built-in predictive power can be effectively used to reduce paging cost.
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