2021/10/31 by Enass Hriba, Hriba, Enass, Matthew C. Valenti +3 · 1 citation
Engineering · #Air Traffic Management and Optimization #FOS: Computer and information sciences #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #UAV Applications and Optimization
paper · pdf · doi:10.48550/arxiv.2111.00603
openalex publication_date 2021/10/31 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
An urban tactical wireless network is considered wherein the base stations\nare situated on unmanned aerial vehicles (UAVs) that provide connectivity to\nground assets such as vehicles located on city streets. The UAVs are assumed to\nbe randomly deployed at a fixed height according to a two-dimensional point\nprocess. Millimeter-wave (mmWave) frequencies are used to avail of large\navailable bandwidths and spatial isolation due to beamforming. In urban\nenvironments, mmWave signals are prone to blocking of the line-of-sight (LoS)\nby buildings. While reflections are possible, the desire for consistent\nconnectivity places a strong preference on the existence of an unblocked LoS\npath. As such, the key performance metric considered in this paper is the\nconnectivity probability, which is the probability of an unblocked LoS path to\nat least one UAV within some maximum transmission distance. By leveraging tools\nfrom stochastic geometry, the connectivity probability is characterized as a\nfunction of the city type (e.g., urban, dense urban, suburban), density of UAVs\n(average number of UAVs per square km), and height of the UAVs. The city\nstreets are modeled as a Manhattan Poisson Line Process (MPLP) and the building\nheights are randomly distributed. The analysis first finds the connectivity\nprobability conditioned on a particular network realization (location of the\nUAVs) and then removes the conditioning to uncover the distribution of the\nconnectivity; i.e., the fraction of network realizations that will fail to meet\nan outage threshold. While related work has applied an MPLP to networks with a\nsingle UAV, the contributions of this paper are that it (1) considers networks\nof multiple UAVs, (2) characterizes the performance by a connectivity\ndistribution, and (3) identifies the optimal altitude for the UAVs.\n