Modeling Heterogeneous Network Interference Using Poisson Point Processes

Abstract

Cellular systems are becoming more heterogeneous with the introduction of low power nodes including femtocells, relays, and distributed antennas. Unfortunately, the resulting interference environment is also becoming more complicated, making evaluation of different communication strategies challenging in both analysis and simulation. Leveraging recent applications of stochastic geometry to analyze cellular systems, this paper proposes to analyze downlink performance in a fixedsize cell, which is inscribed within a weighted Voronoi cell in a Poisson field of interferers. A nearest out-of-cell interferer, out-ofcell interferers outside a guard region, and cross-tier interference are included in the interference calculations. Bounding the interference power as a function of distance from the cell center, the total interference is characterized through its Laplace transform. An equivalent marked process is proposed for the out-of-cell interference under additional assumptions. To facilitate simplified calculations, the interference distribution is approximated using the Gamma distribution with second order moment matching. The Gamma approximation simplifies calculation of the success probability and average rate, incorporates small-scale and largescale fading, and works with co-tier and cross-tier interference. Simulations show that the proposed model provides a flexible way to characterize outage probability and rate as a function of the distance to the cell edge.

Performance Analysis of Small Cells' Deployment under Imperfect Traffic Hotspot Localization

“…3a, we observe that deploying a SC near the MC does not generate additional capacity gains since the interference in this case is very high comparing to the SNR received either from the serving SC or MC. In fact, for a HS in position of less than 300 meters far from the MC, the evaluation of the impact of bad localization of the traffic HS is worthless and not justified because the offloading gain 3 is negative even with a perfect positioning. Hence, the deployment of a SC near the MC does not help to offload the traffic and it deteriorates the throughput in the MC.…”

“…This coefficient is more important when the HS is in the cell edge. Moreover, deploying a SC with errors in the positioning remains a useful solution to offload an 3 The offloading gain is the extra capacity effectively exploited in the deployed SC and it is defined by ρ = η Scenario 2,3 −η Scenario 1 η Scenario 1 important percentage of traffic located in the cell edge. We also notice that for a HS in the cell center, a small percentage of mobile locations can be offloaded by the SC but the mean throughput in the SC (denoted by η Sc2 ) in Scenario 2 is not improved comparing to the mean throughput in Scenario 1 (denoted by η Sc1 which means that the mean user throughput is calculated only in the same region as the served area by the SC in Scenario 2).…”

Massive MIMO Wireless Networks: An Overview

“…Authors in [166] modeled the MIMO HetNets in a fixed cell size (allocation of users to BSs is based on fixed distances). Inter-cell and inter-tier interference were considered in this model approximating the interference distribution using a Gamma function.…”

Stochastic Geometry Based Framework for Coverage and Rate in Heterogeneous Networks with Sectored Fractional Frequency Reuse

“…Recently, tools of stochastic geometry using Point Processes (PP) have been used to capture the non-uniform deployment in Hetnets by averaging over the entire cell coverage area over many spatial realizations [9][10][11][12][13][14]. By assuming base station locations of the different tiers to be given by some certain PP, the SINR distribution of a typically random UE in the plane can be accurately analyzed with minimal numerical complexity and relying only on key network parameters.…”