Location Update versus Paging Trade-Off in Cellular Networks: An Approach Based on Vector Quantization

In this paper, we propose two information-theoretic techniques for efficiently trading off the location update and paging costs associated with mobility management in wireless cellular networks. Previous approaches always attempt to accurately convey a mobile's movement sequence and hence cannot reduce the signaling cost below the entropy bound. Our proposed techniques, however, exploit the rate distortion theory to arbitrarily reduce the update cost at the expense of an increase in the corresponding paging overhead. To this end, we describe two location tracking algorithms based on spatial quantization and temporal quantization, which first quantize the movement sequence into a smaller set of codewords and then report a compressed representation of the codeword sequence. Although the spatial quantization algorithm clusters individual cells into registration areas, the more powerful temporal quantization algorithm groups sets of consecutive movement patterns. The quantizers themselves are adaptive and periodically reconfigure to accommodate changes in the mobile's movement pattern. Simulation study with synthetic and real movement traces for both single-system and multisystem cellular networks demonstrate that the proposed algorithms can reduce the mobile's update frequency to 3-4 updates/day with reasonable paging cost, low computational complexity, storage overhead, and codebook updates.