Mohammadsaleh Nikooroo

In this paper, we focus on offloading of computationally demanding tasks with tight processing delay requirements (hundreds of ms) from user equipments (UEs) to multi-access edge computing servers while exploiting unmanned aerial vehicles (UAVs) as relays. Traditionally addressed problem jointly optimizing an offloading decision and an association of the UEs to the UAVs is, in our work, enriched by optimization of the relaying via UAVs through selection between half-duplex (HD) and full-duplex (FD) under a constraint on the maximum processing delay of individual tasks. To solve this complex problem, we also optimize the length of relaying time slots in HD and the transmission power allocation for the UEs and the UAVs in FD. Then, we transform the problem to one-to-many matching and propose a low-complexity greedy algorithm for the joint offloading decision and UEs association solved together with the duplex selection. We show that our proposal reduces the sum energy consumed by the UEs and UAVs for the tasks offloading by up to 40% compared to related works.

This article investigates the performance of cellular networks assisted by unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs). We focus on a scenario with multihop 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 3-D, 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 nonconvexity 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 this letter, we maximize the minimum downlink capacity of moving users in the mobile networks based on non-orthogonal multiple access (NOMA) with flying base stations (FlyBSs) considering practical constraints on the speed, altitude, and transmitting power of the FlyBSs. We propose a geometrical approach allowing us to reflect the users’ movement and to derive optimal user clustering for NOMA, positions of the FlyBS, and the transmission power allocation to the users in one cluster served in NOMA at the same time-frequency resources. The proposed solution increases the minimum capacity of all users by 20%-59% comparing to state-of-the-art solutions.

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.

The unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) are considered as an efficient way to enhance the capacity of mobile networks. The enhancement provided by such network requires a dynamic positioning of the FlyBSs with respect to the users demands on communication. However, the power consumption of the FlyBS is a challenge due to a limited capacity of the FlyBS's power supply. In this paper, we reduce a total power consumption of the FlyBS while a required communication capacity is guaranteed to the mobile users in every time instant of their movement. To this end, we derive a closed-form solution for an optimization of the FlyBS's total power consumption in every single time step. Then, we provide a numerical solution for the total power optimization problem over a long period of the users’ movement. Via the simulations, we show that the proposed scheme reduces the overall power consumed by the FlyBS significantly (by up to 91%) comparing to existing solutions.

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.