Drone base station (DBS) is a promising technique to extend wireless connections for uncovered users of terrestrial radio access networks (RAN). To improve user fairness and network performance, in this paper, we design 3D trajectories of multiple DBSs in the drone assisted radio access networks (DA-RAN) where DBSs fly over associated areas of interests (AoIs) and relay communications between the base station (BS) and users in AoIs. We formulate the multi-DBS 3D trajectory planning and scheduling as a mixed integer non-linear programming (MINLP) problem with the objective of minimizing the average DBS-to-user (D2U) pathloss. The 3D trajectory variations in both horizontal and vertical directions, as well as the state-of-the-art DBS-related channel models are considered in the formulation. To address the non-convexity and NP-hardness of the MINLP problem, we first decouple it into multiple integer linear programming (ILP) and quasi-convex sub-problems in which AoI association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DBS 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation and a search-based start slot scheduling are considered in the proposed algorithm to improve trajectory design performance and ensure inter-DBS distance constraint, respectively. Extensive simulations are conducted to investigate the impacts of DBS quantity, horizontal speed and initial trajectory on the trajectory planning results. Compared with the static DBS deployment, the proposed trajectory planning can achieve 10-15 dB reduction on average D2U pathloss, and reduce the D2U pathloss standard deviation by 68 , which indicate the improvements of network performance and user fairness.
In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs’ locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45 compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28 enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs. Operating unmanned aerial vehicle (UAV) over cellular networks would open the barriers of remote navigation and far-flung flying by combining the benefits of UAVs and the ubiquitous availability of cellular networks. In this letter, we provide an initial insight on the radio propagation characteristics of cellular-to-UAV (CtU) channel. In particular, we model the statistical behavior of the path-loss from a cellular base station toward a flying UAV. Where we report the value of the path-loss as a function of the depression angle and the terrestrial coverage beneath the UAV. The provided model is derived based on extensive experimental data measurements conducted in a typical suburban environment for both terrestrial (by drive test) and aerial coverage (using a UAV). The model provides simple and accurate prediction of CtU path-loss that can be useful for both researchers and network operators alike. In this paper, we study spectrum sharing of drone small cells (DSCs) network modeled by the 3-D Poisson point process. This paper also investigates an underlay spectrum sharing between the 3-D DSCs network and traditional cellular networks modeled by 2-D Poisson point processes. We take advantage of the tractability of the Poisson point process to derive the explicit expressions for the DSCs coverage probability and achievable throughput. To maximize the DSCs network throughput while satisfying the cellular network efficiency constraint, we find the optimal density of DSCs aerial base stations. Furthermore, we explore the scaling behavior of the optimal DSCs density with respect to the DSCs outage probability constraint under different heights of DSCs. Our analytical and numerical results show that the maximum throughput of the DSCs user increases almost linearly with the increase of the DSCs outage constraint. In order to protect the cellular user, the throughput of the DSCs user stops increasing when it meets the cellular network efficiency loss constraint. To further protect the cellular network in the spectrum underlay, we investigate the effect of primary exclusive regions (PERs) in a 3-D space. Unlike the circular PER in traditional cellular spectrum sharing in the 2-D space, the shape of the 3-D PER is found as a half sphere or a half sphere segment, depending on the radius of PER and the DSCs height limit. We show that the radius of PER should be restricted for small DSCs constraints and limited DSCs height. The densification of small cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul fronthaul network for these small cell base stations. This involves efficient association and placement of the backhaul hubs that connects these small-cells with the core network. Terrestrial hubs suffer from an inefficient non line of sight link limitations and unavailability of a proper infrastructure in an urban area. Realizing the popularity of flying platforms, we employ here an idea of using networked flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned balloons flying at different altitudes, as aerial backhaul hubs. The association problem of these NFP-hubs and small- cell base stations is formulated considering backhaul link and NFP related limitations such as maximum number of supported links and bandwidth. We then present an efficient and distributed solution of the designed problem, which performs a greedy search in order to maximize the sum rate of the overall network. A favorable performance is observed via a numerical comparison of our proposed method with optimal exhaustive search algorithm in terms of sum rate and run-time speed. In this paper, we study coverage probabilities of the UAV-assisted cellular network modeled by 2-dimension (2D) Poisson point process. The cellular user is assumed to connect to the nearest aerial base station. We derive the explicit expressions for the downlink coverage probability for the Rayleigh fading channel. Furthermore, we explore the behavior of performance when taking the property of air-to-ground channel into consideration. Our analytical and numerical results show that the coverage probability is affected by UAV height, pathloss exponent and UAV density. To maximize the coverage probability, the optimal height and density of UAVs are studied, which could be beneficial for the UAV deployment design. We introduce the concept of using unmanned aerial vehicles (UAVs) as drone base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for 5G networks. We first present a system model for forward link transmissions in an IB-IAB multi- tier drone cellular network. We then investigate the key challenges of this scenario and propose a framework that utilizes the flying capabilities of the UAVs as the main degree of freedom to find the optimal precoder design for the backhaul links, user-base station association, UAV 3D hovering locations, and power allocations. We discuss how the proposed algorithm can be utilized to optimize the network performance in both large and small scales. Finally, we use an exhaustive search-based solution to demonstrate the performance gains that can be achieved from the presented algorithm in terms of the received signal to interference plus noise ratio (SINR) and overall network sum-rate.