Resource Management in Wireless Cellular Networks with Heterogeneous Capacity Supply and Heterogeneous Traffic Demand (HetHetNets)

Abstract

The term HetNets (heterogeneous networks) is used to describe cellular networks in which the capacity supply is heterogeneous due to the overlaid architecture of macrocells and small-cells.In this thesis we use the term HetHetNets to refer to cellular networks in which the traffic demand is also heterogeneous due to reasons such as user spatial clustering and diversified user traffic types, in addition to the heterogeneity in the capacity supply.The main objective of this thesis is to study the essential problem of matching the traffic demand ("load") with the capacity supply ("capacity") in resource-limited HetHetNets; this problem is expected to be a major concern in the fifth generation (5G) cellular networks.This thesis proposes two approaches to address this problem. One approach is to deploy small-cells to the centers of user clusters (put supply where needed), and another approach is to guide users to the cells that are lightly loaded (load balancing via spatial traffic shaping).We first investigate how to model the spatial non-uniform (i.e., heterogeneous) user distribution, and then the impact of user spatial heterogeneity on HetNets is evaluated. Simulation-based analyses show that network performance deteriorates significantly with the increase in user spatial heterogeneity if the user locations are uncorrelated with the locations of the macro and small-cell base stations. Cluster analysis on the non-uniform user points is then utilized to find the cluster centroids as the potential locations for small-cells. Simulation results show that the network performance can improve substantially with increasing user spatial heterogeneity if we deploy small-cells in the appropriate locations.The second approach is enabled by the recently developed user-in-the-loop (UIL) paradigm. In the literature, there have been several investigations on load-aware cell association as an approach to match traffic demand with the capacity supply, in which a user may associate to a less loaded cell, even though that cell does not iii necessarily provide the maximum SINR. In other words, a user is associated with a cell to get more share of resources at the cost of lower spectral efficiency. While, the UIL approach can increase the user received SINR and the share of allocated resources at the same time, which encourages a user to move to a new location that maximizes the utility function considering received SINR, cell load and the probability of moving.Numerical results show that the UIL can double the mean user rate in comparison to the load-aware cell association strategy, and also results in a more balanced load across the network.iv