As the fifth generation (5G) of mobile communication systems are becoming a commercial reality, this paper attempts to investigate the reliability and propagation latency performance of UAV-assisted cellular systems that caters to a scalable, flexible and on-demand solution for differentiated 5G services. The reliability performance of the considered system is evaluated in terms of block error rates (BLER) along with the propagation latency for three classes of services: Ultra Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). Considering the impact of fading and shadowing, the closed-form approximate expressions for BLER and propagation latency are evaluated over Rician shadowed fading with various shadowing scenarios. The numerical results reveal important insights related to the achievable reliability performance for three different services under given fading and shadowing severity. Specifically, The tightness of the ap-proximation presented is validated through the Monte-Carlo simulations.

Unmanned Aerial Vehicles (UAVs) are becoming an indispensable tool in civilian and military data-gathering and reconnaissance tasks, due to their ability to freely fly over a mission area and collect data. In this paper, we experimentally demonstrate how off-the-shelf Software-Defined Radio receivers can be used as part of a UAV payload, to create a cost-effective system to locate the origin of a radio transmitter. A uniform linear array of antennas is used to measure the angle-of-arrival of the signal, which is then combined with the GPS coordinates of the UAV and processed with an Unscented Kalman Filter algorithm to estimate the location of the transmitter. We demonstrate this system in field conditions, and show that the system can accurately estimate the angle-of-arrival. We demonstrate that a low-power handset can be located to within 100m from over 1.5km away within several minutes of flight. While our experiment is based around a search and rescue scenario, this localisation approach has wider applicability for wireless communications applications.

Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles. However, the density of vehicles on roads often varies spatially and temporally primarily due to mobility and traffic situations in a geographical area, making it difficult to provide ubiquitous service. Moreover, as energy-constrained UAVs hover in the sky while serving mobile users, they may be faced with interference from nearby UAV cells or other access points sharing the same frequency band, thereby impacting the system’s energy efficiency (EE). Recent multiagent reinforcement learning (MARL) approaches applied to optimise the users’ coverage worked well in reasonably even densities but might not perform as well in uneven users’ distribution, i.e., in urban road networks with uneven concentration of vehicles. In this work, we propose a density-aware communication-enabled multi-agent decentralised double deep Q-network (DACEMAD–DDQN) approach that maximises the total system’s EE by jointly optimising the trajectory of each UAV, the number of connected users, and the UAVs’ energy consumption while keeping track of dense and uneven users’ distribution. Our result outperforms state-of-the-art MARL approaches in terms of EE by as much as 65% – 85%.

Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, points-of-failure in existing infrastructure, or disasters. However, it is challenging to conserve the energy of UAVs during prolonged coverage tasks, considering their limited on-board battery capacity. Reinforcement learning-based (RL) approaches have been previously used to improve energy utilization of multiple UAVs, however, a central cloud controller is assumed to have complete knowledge of the end-devices’ locations, i.e., the controller periodically scans and sends updates for UAV decision-making. This assumption is impractical in dynamic network environments with UAVs serving mobile ground devices. To address this problem, we propose a decentralized Q-learning approach, where each UAVBS is equipped with an autonomous agent that maximizes the connectivity of mobile ground devices while improving its energy utilization. Experimental results show that the proposed design significantly outperforms the centralized approaches in jointly maximizing the number of connected ground devices and the energy utilization of the UAV-BSs.

Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation communications, as they can be deployed to provide wireless connectivity to ground users to supplement existing terrestrial networks. The majority of the existing research into the use of UAV access points for cellular coverage considers rotary-wing UAV designs (i.e. quadcopters). However, we expect fixed-wing UAVs to be more appropriate for connectivity purposes in scenarios where long flight times are necessary (such as for rural coverage), as fixed-wing UAVs rely on a more energy-efficient form of flight when compared to the rotary-wing design. As fixed-wing UAVs are typically incapable of hovering in place, their deployment optimisation involves optimising their individual flight trajectories in a way that allows them to deliver high quality service to the ground users in an energy-efficient manner. In this paper, we propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points while still allowing them to deliver high-quality service to users on the ground. In our decentralized approach, each UAV is equipped with a Dueling Deep Q-Network (DDQN) agent which can adjust the 3D trajectory of the UAV over a series of timesteps. By coordinating with their neighbours, the UAVs adjust their individual flight trajectories in a manner that optimises the total system energy efficiency. We benchmark the performance of our approach against a series of heuristic trajectory planning strategies, and demonstrate that our method can improve the system energy efficiency by as much as 70%.

Providing seamless connectivity to unmanned aerial vehicle user equipment (UAV-UE) is very challenging due to the encountered line-of-sight interference and reduced gains of down-tilted base station (BS) antennas. For instance, as the altitude of UAV-UEs increases, their cell association and handover procedure become driven by the side-lobes of the BS antennas. In this paper, the performance of cellular-connected UAV-UEs is studied under 3D practical antenna configurations. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. For both scenarios, the UAV-UE coverage probability is characterized as a function of the system parameters. The effects of the number of antenna elements on the UAV-UE coverage probability and handover rate of mobile UAV-UEs are then investigated. Results reveal that the UAV-UE coverage probability under a practical antenna pattern is worse than that under a simple antenna model. Moreover, vertically-mobile UAV-UEs are susceptible to attitude handover due to consecutive crossings of the nulls and peaks of the antenna side-lobes.

Unmanned Aerial Vehicles (UAVs) acting as access points in cellular networks require wireless backhauls to the core network. In this paper we employ stochastic geometry to carry out an analysis of the UAV backhaul performance that can be achieved with a network of dedicated ground stations. We provide analytical expressions for the probability of successfully establishing a backhaul and the expected data rate over the backhaul link, given either an LTE or a millimeter-wave backhaul. We demonstrate that increasing the density of the ground station network gives diminishing returns on the performance of the UAV backhaul, and that for an LTE backhaul the ground stations can benefit from being co-located with an existing base station network.

Wireless access points on unmanned aerial vehicles (UAVs) are being considered for mobile service provisioning in commercial networks. To be able to efficiently use these devices in cellular networks it is necessary to first have a qualitative and quantitative understanding of how their design parameters reflect on the service quality experienced by the end user. In this paper we set up a scenario where a network of UAVs operating at a certain height above ground provide wireless service within coverage areas shaped by their directional antennas. We provide an analytical expression for the coverage probability experienced by a typical user as a function of the UAV parameters and demonstrate the performance trade-offs that occur as these parameters are varied.

Stochastic models of base station infrastructure deployment by multiple mobile operators can be an invaluable tool for deriving fundamental results about wireless network sharing. In this paper, we study stochastic geometry models for a shared cellular network consisting of base stations deployed by multiple mobile operators, based on real cellular network data coming from three European countries. Relying on a statistical approach as well as the evaluation of wireless network performance metrics, we show that the log-Gaussian Cox process provides the most compelling fitness results with real multi-operator base station deployment patterns and a model that offers some degree of analytical tractability. The model captures the fact that, in urban areas, there is strong correlation between the locations where the base stations of different operators are deployed. In contrast to that, in rural areas we observe some repulsion between antenna locations of different operators. Moreover, we observe that the behavior which can be modelled with the help of these processes occurs over and over again for similar areas in different countries, which suggests universality of the proposed models.