Department Publications

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

The potential of drone based services is enormous with applications ranging from consumer product delivery to health services. When meeting this demand, one of the major challenges we face is the noise produced by drones, which not only contributes to listener discomfort, but also hinders the device's ability to effectively communicate via audio. Thus, there exists a pressing need for understanding the characteristics of drone related noise, which can then be suppressed using suitable methods. This paper presents a preliminary study on modeling the relationship between input motor current and acoustic noise produced by a drone. An experimental study is conducted indoors for a drone under hovering manoeuvre with a single active motor and propeller. The drone noise was measured by a single on-board microphone. We identify multiple tones or harmonics in the drone noise spectrum that vary proportionally …

We present a novel low cost time-of-flight (ToF) sensor deck for micro UAVs capable of sparse range measurements for localisation and mapping. In disaster scenarios such as building collapses, the larger UAVs equipped with highfidelity sensors such as lidars, structured light cameras and stereo cameras to perceive the environment with high resolution, may have difficulties when navigating through cluttered confined spaces. Micro UAVs are ideal for providing vital data in the aftermath of a disaster scenario but does not have the payload capacity for such sensors. Miniature low cost ToF sensors can provide range measurements albeit with low range and resolution. The proposed solution includes the design and implementation of a novel sensing deck for a commercially available micro UAV with 13 miniature ToF sensors, a low resolution thermal camera and a method to generate reliable 2D maps of GPS denied areas. The sparse observations obtained from range sensors are combined in a Rao-Blackwellized particle filter to create a reliable map. The ToF sensors are arranged to capture a maximum number of points in the horizontal plane at the flying height despite fluctuations in altitude and orientation. Particle weighting and resampling are only performed in a loop closure or when moving through a known area. Importance weight for the particles are computed by an Iterative Closest Point based method by calculating the error between nearest scan points. The proposed method has been tested in indoor environments and verified as a robust method to generate reliable maps of unknown environments.