Planning UAV Activities for Efficient User Coverage in Disaster Areas

Climate changes brought about by global warming as well as man-made environmental changes are often the cause of sever natural disasters. ICT, which is itself responsible for global warming due to its high carbon footprint, can play a role in alleviating the consequences of such hazards by providing reliable, resilient means of communication during a disaster crisis. In this paper, we explore the provision of wireless coverage through UAVs (Unmanned Aerial Vehicles) to complement, or replace, the traditional communication infrastructure. The use of UAVs is indeed crucial in emergency scenarios, as they allow for the quick and easy deployment of micro and pico cellular base stations where needed. We characterize the movements of UAVs and define an optimization problem to determine the best UAV coverage that maximizes the user throughput, while maintaining fairness across the different parts of the geographical area that has been affected by the disaster. To evaluate our strategy, we simulate a flooding in San Francisco and the car traffic resulting from people seeking safety on higher ground

Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs

Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning algorithm relies on a map uncertainty metric to collect measurements at those locations where they are more informative. An online Bayesian learning algorithm is developed to update the map estimate and uncertainty metric every time a new measurement is collected, which enables real-time operation.Comment: 6 pages, 2 figures, submitted to the IEEE MLSP 202

Spatial Configuration of Agile Wireless Networks with Drone-BSs and User-in-the-loop

Agile networking can reduce over-engineering, costs, and energy waste. Towards that end, it is vital to exploit all degrees of freedom of wireless networks efficiently, so that service quality is not sacrificed. In order to reap the benefits of flexible networking, we propose a spatial network configuration scheme (SNC), which can result in efficient networking; both from the perspective of network capacity, and profitability. First, SNC utilizes the drone-base-stations (drone-BSs) to configure access points. Drone-BSs are shifting paradigms of heterogeneous wireless networks by providing radically flexible deployment opportunities. On the other hand, their limited endurance and potential high cost increase the importance of utilizing drone-BSs efficiently. Therefore, secondly, user mobility is exploited via user-in-the-loop (UIL), which aims at influencing users' mobility by offering incentives. The proposed uncoordinated SNC is a computationally efficient method, yet, it may be insufficient to exploit the synergy between drone-BSs and UIL. Hence, we propose joint SNC, which increases the performance gain along with the computational cost. Finally, semi-joint SNC combines benefits of joint SNC, with computational efficiency. Numerical results show that semi-joint SNC is two orders of magnitude times faster than joint SNC, and more than 15 percent profit can be obtained compared to conventional systems.Comment: To appear in IEEE Transactions on Wireless Communication

Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.Comment: 9 pages, 13 figures, to be presented in Proc. IEEE INFOCOM 201

Sense-Store-Send: Trajectory Optimization for a Buffer-aided Internet of UAVs

In this letter, we study a buffer-aided Internet of unmanned aerial vehicles (UAVs) in which a UAV performs data sensing, stores the data, and sends it to the base station (BS) in cellular networks. To minimize the overall completion time for all the sensing tasks, we formulate a joint trajectory, sensing location, and sensing time optimization problem. To solve this NP-hard problem efficiently, we propose an iterative trajectory, sensing location and sensing time optimization (ITLTO) algorithm, and discuss the trade-off between sensing time and flying time. Simulation results show that the proposed algorithm can effectively reduce the completion time for the sensing tasks.Comment: Accepted by IEEE Communications Letter

Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design

A novel framework is proposed for quality of experience (QoE)-driven deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex three-dimensional (3D) deployment and dynamic movement of the UAVs is formulated for maximizing the sum mean opinion score (MOS) of ground users, which is proved to be NP-hard. In the aim of solving this pertinent problem, a three-step approach is proposed for attaining 3D deployment and dynamic movement of multiple UAVs. Firstly, genetic algorithm based K-means (GAK-means) algorithm is utilized for obtaining the cell partition of the users. Secondly, Q-learning based deployment algorithm is proposed, in which each UAV acts as an agent, making their own decision for attaining 3D position by learning from trial and mistake. In contrast to conventional genetic algorithm based learning algorithms, the proposed algorithm is capable of training the direction selection strategy offline. Thirdly, Q-learning based movement algorithm is proposed in the scenario that the users are roaming. The proposed algorithm is capable of converging to an optimal state. Numerical results reveal that the proposed algorithms show a fast convergence rate after a small number of iterations. Additionally, the proposed Q-learning based deployment algorithm outperforms K-means algorithms and Iterative-GAKmean (IGK) algorithms with a low complexity

Joint Altitude and Beamwidth Optimization for UAV-Enabled Multiuser Communications

In this letter, we study multiuser communication systems enabled by an unmanned aerial vehicle (UAV) that is equipped with a directional antenna of adjustable beamwidth. We propose a fly-hover-and-communicate protocol where the ground terminals (GTs) are partitioned into disjoint clusters that are sequentially served by the UAV as it hovers above the corresponding cluster centers. We jointly optimize the UAV's flying altitude and antenna beamwidth for throughput optimization in three fundamental multiuser communication models, namely UAV-enabled downlink multicasting (MC), downlink broadcasting (BC), and uplink multiple access (MAC). Our results show that the optimal UAV altitude and antenna beamwidth critically depend on the communication model considered.Comment: to appear in IEEE Communications Letter

Robust Resource Allocation for UAV Systems with UAV Jittering and User Location Uncertainty

In this paper, we investigate resource allocation algorithm design for multiuser unmanned aerial vehicle (UAV) communication systems in the presence of UAV jittering and user location uncertainty. In particular, we jointly optimize the two-dimensional position and the downlink beamformer of a fixed-altitude UAV for minimization of the total UAV transmit power. The problem formulation takes into account the quality-of-service requirements of the users, the imperfect knowledge of the antenna array response (AAR) caused by UAV jittering, and the user location uncertainty. Despite the non-convexity of the resulting problem, we solve the problem optimally employing a series of transformations and semidefinite programming relaxation. Our simulation results reveal the dramatic power savings enabled by the proposed robust scheme compared to two baseline schemes. Besides, the robustness of the proposed scheme with respect to imperfect AAR knowledge and user location uncertainty at the UAV is also confirmed.Comment: 6 pages, 4 figures, accepted by Proc. IEEE GLOBECOM 2018 Workshop

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities

The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. In specific, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone-cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure