Unmanned aerial vehicle (UAV) is a key enabler for communication systems beyond the fifth generation due to its applications in almost every field, including mobile communications and vertical industries. However, there exist many challenges in 3-D UAV placement, such as resource and power allocation, trajectory optimization, and user association. This problem becomes even more complex as UAV changes its height, which in turn varies the channel conditions and reduces the coverage on account of high co-channel interference. To maximize the user coverage in uplink transmission, we propose to jointly optimize the 3-D UAV placement and path-loss compensation factor. Moreover, we also optimize the latter for various UAV deployment heights in the suburban environment. Simulation results have demonstrated that the joint optimization of the UAV height and path-loss compensation factor results in better coverage and throughput performance as compared to the baseline scheme.

Joint Optimization of UAV 3-D Placement and Path-Loss Factor for Energy-Efficient Maximal Coverage

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

5G-Enabled UAV-to-Community Offloading: Joint Trajectory Design and Task Scheduling

Unmanned-aerial-vehicle (UAV)-enabled mobile-edge computing (MEC) has emerged as a promising paradigm to extend the coverage of computation service for Internet of Things (IoT) applications, which are usually time sensitive and computation intensive. In this article, a novel design framework is proposed for a multi-UAV-enabled MEC system, where edge servers are equipped on multiple UAVs to provide flexible computation assistance to IoT devices with hard deadlines. The aim is to maximize the number of served IoT devices through jointly optimizing UAV trajectory and service indicator as well as resource allocation and computation offloading, where the chosen IoT devices will complete their computation tasks on time under given energy budgets and co-channel interference is taken into account. We formulate the optimization problem as a mixed integer nonlinear programming (MINLP), which is challenging to solve directly. The problem is first reformulated to a more mathematically tractable form by adding a penalty term to the objective function. We then decouple the problem into two subproblems and develop an iterative algorithm by solving the two subproblems with alternating optimization and successive convex approximation techniques, where the proposed algorithm converges to a Karush–Kuhn–Tucker (KKT) solution. In addition, an efficient initialization scheme is proposed based on multiple traveling salesman problem with time windows (m-TSPTWs) method. Finally, simulation results are provided to demonstrate that the proposed joint design achieves significant performance gains over baseline schemes.

Multi-UAV-Enabled Mobile-Edge Computing for Time-Constrained IoT Applications

With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.

UAV-LEO Integrated Backbone: A Ubiquitous Data Collection Approach for B5G Internet of Remote Things Networks

Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm’s convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.

Joint Resource Allocation and Trajectory Optimization With QoS in UAV-Based NOMA Wireless Networks

The wireless communication field is approaching the realization of the integrated unmanned aerial vehicle (UAV) and cellular network for further improved communication performance. In this paper, we consider a cellular network with a UAV-mounted aerial base station (ABS) coexisting with multiple terrestrial base stations (TBSs), where each base station (BS) is serving multiple users. Under the probabilistic channel based environment, we design the three-dimensional (3D) positioning for the ABS and the transmit power allocation for all the nodes in the uplink (UL), downlink (DL), and combined UL and DL operations. Considering the ABS user (AU) and the TBS user (TU) as contending parties, our objective is to maximize the weighted sum of the minimum data rate of the AUs and the minimum data rate of the TUs. The whole optimization problem is non-convex and difficult to be directly solved, for which we employ the block coordinate descent (BCD), the successive convex approximation (SCA), the particle swarm optimization (PSO), and the discrete search algorithm (DSA) methods. Numerical results shed some light on interesting observations regarding the comparison between our solution and benchmark schemes, as well as the optimal 3D position of the ABS in UL, DL, and combined UL and DL operations.

UAV Placement and Power Allocation in Uplink and Downlink Operations of Cellular Network

Software-defined networking (SDN) is a cutting-edge technology, featuring a centralized control that facilitates, e.g., flexible deployment of unmanned aerial vehicles (UAVs). On the other hand, UAV-enabled communication is a promising technology due to its numerous traits such as the ability of on-demand deployment and the high likelihood of strong line-of-sight (LoS) communication links. However, UAV-enabled communication suffers non-perpetual nodes and intermittent communication links. Fortunately, SDN provisions an unparalleled global vision on such dynamic network architecture to overcome the challenges of loose links and disappearing nodes. On the other hand, there are catastrophic or remote regions where a UAV-mounted base station (BS) potentially functions without a terrestrial BS, albeit a WiFi access point (AP) may still exist. Therefore, this paper considers software-defined coexisting UAV-mounted BS (UBS) and WiFi AP, and investigates the queuing delay behavior via the UBS positioning and the AP traffic offloading. The subscribers (users) are divided into cellular subscribers (CSs) and WiFi subscribers (WSs). A CS is connected to the UBS and is possibly granted simultaneous access to the AP, leading to WiFi traffic offloading. Leveraging the software-defined global view, the objective of a software-defined controller (SDC) is to minimize the average M/M/1 queuing delay of the CSs, while guaranteeing the delay performance for the WSs, via optimizing the spectrum allocation, the UBS position, the CS association to the AP, and the CS traffic offloading. The optimization problem is non-convex, comprising binary variables, for which we use the block coordinate descent and successive convex approximation methods to find a high-quality solution. Numerical results verify that our solution achieves significant performance gains over benchmark schemes.

Software-Defined Coexisting UAV and WiFi: Delay-Oriented Traffic Offloading and UAV Placement

Aiding cellular networks by unmanned aerial vehicles (UAVs) is a leap forward to address the ever-rising, multifarious, and dynamic traffic demands. The UAV utilization in wireless communication presents essential advantages, such as position control and appealing Line-of-Sight (LoS) components. In this article, we consider a UAV deployed as an aerial base station (ABS) to assist a terrestrial base station (TBS), serving several users in a hotspot area, via user offloading. Considering unavailable knowledge of the user’s locations, the ABS is assumed to hover at the cell center (geometric center) above the TBS as a best-effort location for all the users. Taking into account the mutual interference between the aerial and terrestrial communication links due to spectrum reuse, we study the impact of the ABS altitude and transmit power, as well as the offload portion, on the user’s downlink sum rate under the settings of the LoS channel. The results show that the optimal values for the design variables are either the maximum or the minimum boundaries. Also, the results demonstrate that our method outperforms benchmark schemes with appreciable amounts of gain. We also investigate the method’s behavior with the ABS horizontal position optimization, probabilistic LoS channel, uplink communication, and orthogonal spectrum allocation.

UAV-Aided Cellular Operation by User Offloading

This paper studies an unmanned aerial vehicle (UAV)-enabled cellular system coexisting with WiFi network. By spectrum sharing and traffic offloading with the WiFi access point, our objective is to minimize the average delay of the users served by the UAV base station, while ensuring that the delay of the WiFi users is not greater than certain threshold, via jointly optimizing the spectrum allocation, the set of offloaded users and their offloaded traffic rates. The optimization problem is non-convex and difficult to be directly solved. We propose efficient sub-optimal solution via block coordinate descent method. Numerical results are provided to validate the proposed design.

Delay-Oriented Spectrum Sharing and Traffic Offloading in Coexisting UAV-Enabled Cellular and WiFi Networks

The wireless communication field is getting closer to the integration of the unmanned aerial vehicle (UAV) and cellular network for further enhanced service quality. In this paper, we study a wireless communication system with coexisting aerial and terrestrial base stations, which respectively serve their associated users. By taking into account the mutual interference between the aerial and terrestrial communication links, we study the impact of the aerial base station (ABS) altitude and transmit power on the system's downlink and uplink data rates. In many situations, the results show that the optimal ABS altitude and transmit power are either the maximum or the minimum possible values depending on, for instance, the relative proximity between the ABS, the ABS user (AU), and the terrestrial base station user (TU).

Muntadher A. Ali

Papers

UAV Placement and Power Allocation in Uplink and Downlink Operations of Cellular Network

Software-Defined Coexisting UAV and WiFi: Delay-Oriented Traffic Offloading and UAV Placement

UAV-Aided Cellular Operation by User Offloading

Delay-Oriented Spectrum Sharing and Traffic Offloading in Coexisting UAV-Enabled Cellular and WiFi Networks

Altitude and Power Optimization for Coexisting Aerial and Terrestrial Base Stations.