HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

TL;DR

This paper introduces a novel, adjustable spatial traffic model for heterogeneous wireless cellular networks that captures the heterogeneity and correlation between user and base station locations, improving upon traditional PPP-based models.

Contribution

The paper proposes a simple, non-parameterized model with two statistical parameters to accurately represent traffic heterogeneity and correlation in HetNets, enhancing modeling flexibility.

Findings

The model effectively captures the impact of traffic heterogeneity on network performance.

Adjusting the statistical parameters influences the spatial distribution and network metrics.

The approach improves the realism of traffic modeling in HetNets.

Abstract

A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipments (UEs). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (non-uniformity) in the UE locations. Besides, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations. This paper presents a novel heterogeneous spatial traffic modeling which allows statistical adjustment. Simple and non-parameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second-moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross-moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).