This paper investigates the performance of cellular networks assisted by unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs). We focus on a scenario with multi-hop relaying via FlyBSs to deliver data from a ground base station (GBS) to users in a challenging case with the channels reused at all hops to exploit radio resources efficiently. Our objective is to maximize the sum capacity of the users via an optimization of FlyBSs’ position in 3D, association of users to either GBS or to one of the FlyBSs, allocation of channels for communication at individual hops, and allocation of transmission power for all channels. Moreover, practical constraints on the FlyBSs’ movement, transmission and propulsion power, and backhaul capacity are taken into account. Due to a non-convexity and discreetness of the objective and some constraints, there is no optimal solution to the formulated problem. Thus, we propose an analytical solution based on an alternating optimization of an energy-efficient placement of the FlyBSs, channel allocation, user association, and transmission power. Each subproblem in the alternating optimization is substituted either by a linear programming (LP) problem through a change of variables, or by a convex problem via a conversion of the objective and constraints. The results show an increase in sum capacity by 35%–60% compared to related works while the FlyBSs’ propulsion power consumption is not increased.

The latency associated with accessing data stored on edge computing servers for vehicles encompasses both the communication between a vehicle and a server as well as a latency of a data storage system. To enable low-latency vehicular services, an efficient resource management should consider the communication as well as the storage I/O cache resource allocation along with a data access pattern and a priority of individual vehicular services. Therefore, we focus on a joint optimization of communication and storage I/O cache resource allocation for access to data of vehicular services hosted by the edge computing servers. The proposed framework determines the data placement for the services and allocates communication and storage I/O cache resources to each service. The objective is to minimize the overall latency experienced by vehicular services for access to data. The edge computing platforms share storage and communication resources among various vehicular services, each having distinct priorities and data access rates or patterns. Hence, to reflect different priorities of services in resource allocation, our objective metric takes into account the service priority, data access frequency, and latency. We propose a feasible solution using dual relaxation considering both communication and storage latencies. The proposed solution reduces the average latency of vehicular services by up to 1.8x compared to the state-of-the-art resource allocation method for vehicular edge computing. Even more notable improvement is observed for high priority vehicular services, where the proposal leads to 2.5x lower latency compared to the state-of-the-art storage I/O cache architecture for virtualized cloud services.

In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed. The problem is formulated under constraints on the FlyBS's flying speed, propulsion power consumption, and transmission power for both of flying and ground base stations. None of the existing solutions maximizing the sum capacity can be applied due to the combination of these practical constraints. This paper pioneers in an inclusion of all these constraints together with backhaul to derive the optimal 3D positions of the FlyBS and to optimize the transmission power allocation for the channels at both backhaul and access links as the users move over time. The proposed solution is geometrical based, and it shows via simulations a significant increase in the sum capacity (up by 19%-47%) compared with baseline schemes where one or more of the aspects of backhaul communication, transmission power allocation, and FlyBS's positioning are not taken into account.

Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of wireless networks. Such potential enhancement, however, relies on an efficient positioning of the FlyBSs as well as a management of resources. In this paper, we study the problem of sum capacity maximization in an extended model for mobile networks where multiple FlyBSs are deployed between the ground base station and the users. Due to an inclusion of multiple hops, the existing solutions for two-hop networks cannot be applied due to the incurred backhaul constraints for each hop. To this end, we propose an analytical approach based on an alternating optimization of the FlyBSs' 3D positions as well as the association of the users to the FlyBSs over time. The proposed optimization is provided under practical constraints on the FlyBS's flying speed and altitude as well as the constraints on the achievable capacity at the backhaul link. The proposed solution is of a low complexity and extends the sum capacity by 23%-38% comparing to state-of-the-art solutions.

The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) served by the FlyBSs while a minimum required capacity to every device is guaranteed. To this end, we propose a geometrical approach allowing to derive the 3D positions of the FlyBS over time as the IoTDs move and we determine the transmission power allocation for the IoTDs. The problem is formulated and solved under practical constraints on the FlyBS's transmission and propulsion power consumption as well as on flying speed. The proposed solution is of a low complexity and increases the sum capacity by 15% -46% comparing to state-of-the-art works.

Flying base stations (FlyBSs) can serve space-time varying heterogeneous traffic in the areas, where a deployment of conventional static base stations is uneconomical or unfeasible. We focus on energy consumption of the FlyBSs serving moving users. For such scenario, rotary-wing FlyBSs are not efficient due to a high energy consumption while hovering at a fixed location. Hence, we consider airship-based FlyBSs. For these, we derive an analytical relation between the sum capacity of the users and the energy spent for flying. We show theoretical bounds of potential energy saving with respect to a relative sum capacity guarantee to the users for single FlyBS. Then, we generalize the problem towards multiple FlyBSs and we propose an algorithm minimizing the energy consumption of the FlyBSs serving moving users under a constraint on the minimum relative sum capacity guarantee. The proposed algorithm reduces the energy consumed by the airship-based FlyBSs for flying by dozens of percent at a cost of only a marginal and controlled degradation in the sum capacity. For example, if the degradation in the sum capacity up to 1% is allowed, 55.4%, 67.5%, and 90.7% of the energy is saved if five, three, and one FlyBSs are deployed, respectively.

Unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) are considered as an efficient tool to enhance the capacity of future mobile networks and to facilitate the communication in emergency cases. These benefits are, however, conditioned by an efficient control of the FlyBSs and management of radio resources. In this paper, we propose a novel solution jointly selecting the optimal clusters of an arbitrary number of the users served at the same time-frequency resources by means of non-orthogonal multiple access (NOMA), allocating the optimal transmission power to each user, and determining the position of the FlyBS. This joint problem is constrained with the FlyBS’s propulsion power consumed for flying and with a continuous guarantee of a minimum required capacity to each mobile user. The goal is to enhance the duration of a communication coverage in NOMA defined as the time interval within which the FlyBS always provides the minimum required capacity to all users. The proposed solution clusters the users and allocates the transmission power of the FlyBS to the users efficiently so that the communication coverage provided by the FlyBSs is extended by 67%–270% comparing to existing solutions while the propulsion power is not increased.

Deployment of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs) is considered as an efficient tool to enhance capacity of mobile networks and to facilitate communication in emergency cases. The improvement provided by such network requires a dynamic positioning of the FlyBSs with respect to the mobile users. In this paper, we focus on an optimization of transmission power of the FlyBS in networks with non-orthogonal multiple access (NOMA). We propose a solution jointly positioning the FlyBS and selecting the optimal grouping of users for NOMA in order to minimize the FlyBS's transmission power under the constraint on guaranteeing a minimum required capacity for the mobile users. Moreover, we derive the grouping of users corresponding to the optimal transmission power in a low-degree polynomial time, which makes it suitable for real-time applications. According to the simulations, the proposed method brings up to 31% of FlyBS's transmission power saving compared to existing solutions.

Unmanned Aerial Vehicles (UAVs), acting as flying base stations (FlyBSs), are seen as a promising solution for future mobile networks, as the FlyBSs can serve space and time varying heterogeneous traffic in areas where deployment of conventional static base stations is uneconomical or infeasible. However, an energy consumption of the FlyBSs is a critical issue. In this paper, we target a scenario where the FlyBSs serve slowly moving users, e.g., visitors of an outdoor music festival or a performance. In such scenario, rotary-wing FlyBSs are not efficient due to a high energy consumption while not moving (given by an effect of a “helicopter” dynamics). Hence, we consider small airships or balloons. We develop a closed-form solution that determines new positions of the FlyBSs so that the energy consumption for a movement of the FlyBSs is reduced significantly (by 45-94% depending on the number of deployed FlyBSs) while sum capacity of the users is decreased only marginally (less than 1% for before-mentioned energy savings). Moreover, the proposed solution does not require any prediction of users' movement, thus, it is not affected by the prediction error or uncertainty of the users' behavior.

Unmanned aerial vehicles acting as flying base stations (FlyBSs) have been considered as an efficient tool to enhance capacity of mobile networks and to facilitate communication in emergency cases. The enhancement provided by such network necessitates a dynamic positioning of the FlyBSs with respect to the users. Despite that, the power consumption of the FlyBS remains an important issue to be addressed due to limitations on the capacity of FlyBS’s batteries. In this paper, we propose a novel solution combining a transmission power control and the positioning of the FlyBS in order to ensure quality of service to the users while minimizing total consumed power of the FlyBS. We derive a closed-form solution for joint transmission and propulsion power optimization in a single future step. Moreover, we also provide a numerical method to solve the joint propulsion and transmission power optimization problem when a realistic (i.e. inaccurate) prediction of the users’ movement is available. According to the simulations, the proposed scheme brings up to 26% of total FlyBS’s power saving compared to existing solutions.

Deployment of unmanned aerial vehicles (UAVs) in future mobile networks has recently been considered as a reliable technique to enhance capacity of the network and to facilitate an efficient communication in emergency cases. The improvement provided by such network depends on the deployment of the UAVs with respect to users and also on the power consumption of the UAV. In this paper, we study the power consumption in the wireless networks equipped with the UAVs. We propose the novel solution in which the UAV can either change its transmitting power or relocate itself to a new position as the users move in order to guarantee quality of service to the users. We analytically find the transmitting power and an ideal position of the UAV to minimize the total consumed power by the UAV consisting of the power for communication and the power for a displacement of the UAV. According to the simulations, the proposed scheme brings up to 30% of total UAV power saving in the scenario with users moving in crowd.