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A practical information theoretic framework is developed for studying the optimal tradeoff between location update and paging costs in cellular networks. The framework envisions the quantization of location information into a registration area (RA) level granularity, followed by the use of an entropy-coding technique to decrease the location update rate. The rate distortion theory of the lossy quantization is identified as an appropriate measure for capturing the optimal tradeoff between a mobile's update rate and its location uncertainty. Based on LZ-78 compression, two different RA-level location update algorithms (RA-LeZi and LeZi-RA) have been developed, both of which asymptotically approach this rate-distortion bound. By allowing for quantization loss in the mobile node's movement pattern, this framework can reduce the overall update cost below the entropy bound associated with the original loss-less LeZi-update mobility management algorithm. Simulation results demonstrate a sharp decrease (∼ 50%) in the update cost, at the expense of a minor (∼ 25%) increase in the overall location management costs. The key essence of this framework lies in its practical applicability, because today's wireless networks already track the mobile user at an RA-level granularity.