Integrating unmanned aerial vehicles (UAVs) as the base station is a recent progress and a promising solution to assist cellular networks to improve coverage. In this paper, an analytical framework is proposed to analyze coverage probability and capacity, using stochastic geometry. We incorporate the air-to-ground (A2G), and two-piece path loss model that undergoes millimeter wave (mmWave) channel fading in both line-of-site (LOS) and non-line-of-site (NLOS). Furthermore, we assume that all users’ equipment’s (UEs) are clustered around Terrestrial Base Stations (TBS) and UAVs, according to Poisson Cluster Process (PCP), i.e., Matern cluster process. We also propose Multiple-Input-Multiple-Output (MIMO) transmission where multiple single-antenna UAVs simultaneously communicate with TBS. Initially, we obtain expressions for association probability with each tier. After that, we derive downlink coverage expression based on signal-to-interference-plus-noise-ratio (SINR), by assuming 3D directional beamforming with UAV height being not fixed. Numerical results demonstrate the efficiency and accuracy of the proposed approach.

While LTE (4G) networks offer high-speed data transmission, 5G is necessary to meet the growing demands of modern communication. The main goals of 5G are to make communications more agile by significantly increasing network capacity, lowering latency, and speeding up the networks to handle a massive number of connected devices. To materialize this vision, a new concept has emerged in 5G called network slicing; hence, network providers can offer tailored quality of service based on every type of user according to their specific requirements. However, efficient resource allocation in LTE and across slices or even within a slice in 5G systems to meet QoS is still one of the most critical issues for network designers while resources change over the complexity, dynamic nature, and resource constraints of the network. Classic resource allocation methods cannot cope with the dynamics of the changing network environment; hence, there is an increasing need for faster and smarter resource allocation schemes. This paper proposes an efficient dynamic scheduling algorithm based on machine learning methods for real-time applications in LTE networks, extending to 5G using Radio Access Network (RAN) slicing with a focus on enhanced Mobile Broadband (eMBB) slices. Carrying out extensive simulations and evaluations by using the ns-3 simulator, we compare and analyze various downlink scheduling rules in terms of system throughput, spectral efficiency, average delay, and packet loss ratio. The simulation results show that the proposed algorithm always outperforms the QoS performance, especially for real-time applications.

In dense cities, vertical heterogeneous networks (VHetNets) operating in the millimeter wave (mmWave) bands can deliver massive data rates and bridge coverage gaps, but are prone to 3D blockages and suffer from high penetration loss. Drawing on stochastic geometry theory, this paper studies the downlink performance in a mmWave network of Ground Bases Stations (GBSs) assisted by a finite number of Unmanned Aerial Vehicle (UAV)-Base Stations (UBSs) under 3D blockage effects of buildings. The UBSs are distributed according to Binomial Point Process within a bounded circular area. To mitigate the significant propagation loss, we deploy multiuser multiple-input-multiple-output (MU-MIMO) at the UBS. The buildings are modeled by a Boolean scheme of rectangles with a height following exponential distribution. To depict a hotspot scenario, the users (UEs) distribution is modeled as a Thomas Cluster Process and gathered around the GBSs. Then, we have developed formulas for LoS/NLoS probabilities, that take into account the density, height, and area of blockages. Assuming shadowing, Nakagami- m fading channels, and using the gamma approximation, we evaluate the coverage probability and area spectral efficiency (ASE). In addition, a 3D beamforming analysis is performed. The analytical results are validated through Monte Carlo simulations. The findings reveal that in dense areas, UBSs can improve performance with an optimal number, altitude, and tilt angle, that depend on the target area and the city's blockage density index. Furthermore, MU-MIMO transmission enhances the ASE.