Statistical Analysis of Self-Organizing Networks With Biased Cell Association and Interference Avoidance

Abstract

In this paper, we assess the viability of heterogeneous networks composed of legacy macrocells, which are underlaid with self-organizing picocells. Aiming to improve coverage, cell-edge throughput, and overall system capacity, self-organizing solutions, such as range expansion bias, an almost blank subframe (ABS), and distributed antenna systems, are considered. Herein, stochastic geometry is used to model network deployments, whereas higher order statistics through the cumulants concept is utilized to characterize the probability distribution of received power and aggregate interference at the user of interest. A comprehensive analytical framework is introduced to evaluate the performance of such self-organizing networks in terms of outage probability and average channel capacity with respect to the tagged receiver. To conduct our studies, we consider a shadowed fading channel model incorporating lognormal shadowing and Nakagami-m fading. Results show that the analytical framework matches well with numerical results obtained from Monte Carlo simulations. We also observed that by simply using ABSs, the aggregate interference at the tagged receiver is reduced by about 8 dB. Although more elaborated, interference control techniques such as downlink bitmap and distributed antennas systems become needed when the density of picocells in the underlaid tier gets high.