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

摘要

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 attempt to always accurately convey a mobileu00026;#8217;s movement sequence and hence, cannot reduce the signaling cost below the entropy bound. Our proposed techniques, however, exploit 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. While 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 mobileu00026;#8217;s movement pattern. Simulation study with synthetic as well as real movement traces for both single-system and multi-system cellular networks demonstrate that the proposed algorithms can reduce the mobileu00026;#8217;s update frequency to 3-4 updates/day with reasonable paging cost, low computational complexity, storage overhead and codebook updates.