Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations

Abstract

In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this by adopting successive convex approximation (SCA), geometric programming (GP), and mesh adaptive direct search (MADS) to solve each of the resulting problems, and find power allocation, altitudes of ABSs, and assignment parameters, respectively. Simulation results reveal that our proposed scenario can improve the overall network profit by up to 47 percent compared to the case where the TPs and ABS altitudes are fixed. Besides, finding the ABS altitude with fixed TPs can improve the network profit by 20 percent compared to the power allocation case with fixed ABS altitudes. Our proposed heterogeneous approach improves the network profit by up to 18, 16, 15, and 10 percent in suburban, urban, dense urban, and high-rise urban environments, respectively, compared to the cases with homogeneous ABSs.