One of the main expected characteristics of the envisioned 5G wireless cellular networks is heterogeneity. Heterogeneity is expected in both supply and demand. In the supply side, the network access part will be comprised of heterogeneous base stations (BSs) with different transmit powers, antenna heights, and radio technologies including macro-BSs, pico-BSs, femto-BSs, and wi-fi access points (HetNets). The spatial distribution of BSs is also heterogeneous (non-uniform) since the deployment of BSs is not carefully planned anymore and follows the customer requirements. In the demand side, the distribution of user equipments (UEs) is heterogeneous in the time domain as well as in the space domain due to the emergence of various applications with different rate requirements such as machine type communications (MTC) and also the heterogeneity of population density specially in municipal areas.

Nevertheless, an enormous majority of the existing literature on traffic modeling in wireless cellular networks consider only homogeneous (uniform) traffic scenarios. In particular, two independent Poisson point processes (PPPs) are excessively used to model the spatial distribution of UEs and BSs. PPP might be a fitting process for BSs but it is not an accurate model for the UE distributions. The assumption of independence between BSs and UEs is also not realistic since BSs (specially small-cell BSs) are usually deployed in UE hotspots.

In this thesis, we propose an accurate, realistic, simple, and adjustable modeling for the future heterogeneous wireless cellular networks with heterogeneous traffic distributions (HetHetNets). First, we propose a traffic modeling process describing a systematic approach to traffic modeling. According to the proposed process, we introduce a traffic modeling in which the heterogeneity of the UE distribution as well as the correlation between UEs and BSs are adjustable. Then, we show the impact of the traffic heterogeneity and the UE-BS correlation on the performance of HetHetNets. Finally, we present algorithms and applications in wireless networks which can exploit this realistic traffic modeling to enhance the network performance.