To understand the spatial deployment of base stations (BSs) is the first stepto facilitate the performance analyses of cellular networks, as well as thedesign of efficient networking protocols. Poisson point process (PPP) has beenwidely adopted to characterize the deployment of BSs and established thereputation to give tractable results in the stochastic geometry analyses.However, given the total randomness assumption in PPP, its accuracy has beenrecently questioned. On the other hand, the actual deployment of BSs during thepast long evolution is highly correlated with heavy-tailed human activities.The {lpha}-stable distribution, one kind of heavy-tailed distributions, hasdemonstrated superior accuracy to statistically model the spatial density ofBSs. In this paper, we start with the new findings on {lpha}-stabledistributed BSs deployment and investigate the intrinsic feature (i.e., thespatial self-similarity) embedded in the BSs. Based on these findings, weperform the coverage probability analyses and provide the relevant theoreticalresults. In particular, we show that for some special cases, our work couldreduce to the fundamental work by J. G. Andrews. We also examine the networkperformance under extensive simulation settings and validate that thesimulations results are consistent with our theoretical derivations.
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