Exploring UAV Networking from the Terrain Information Completeness Perspective: A Tutorial

TL;DR

The article addresses UAV deployment in wireless networks through the lens of terrain information completeness, proposing a threefold taxonomy (complete, incomplete, no terrain information) and analyzing how topology and deployment stage interact under each. It reviews complete-terrain techniques for terrain construction and dynamic trajectory design, then pairs incomplete-terrain modeling with a stochastic-geometry framework to assess LoS/blockage and performance, and finally extends real-time no-information methods to multi-user settings. Three case studies anchor the analysis: UAV-aided terrain construction with dynamic tracking, SG-based evaluation of LoS probability and coverage, and a generalized real-time no-information search for multi-user scenarios, complemented by a qualitative discussion on charging, backhaul, and EMF exposure. The findings highlight where SG-based analytics approximate reality, demonstrate trade-offs between information availability and performance, and provide practical guidance for deploying UAV networks in varied terrain-information regimes.

Abstract

Terrain information is a crucial factor affecting the performance of unmanned aerial vehicle (UAV) networks. As a tutorial, this article provides a unique perspective on the completeness of terrain information, summarizing and enhancing the research on terrain-based UAV deployment. In the presence of complete terrain information, two highly discussed topics are UAV-aided map construction and dynamic trajectory design based on maps. We propose a case study illustrating the mutually reinforcing relationship between them. When terrain information is incomplete, and only terrain-related feature parameters are available, we discuss how existing models map terrain features to blockage probabilities. By introducing the application of this model with stochastic geometry, a case study is proposed to analyze the accuracy of the model. When no terrain information is available, UAVs gather terrain information during the real-time networking process and determine the next position by collected information. This real-time search method is currently limited to relay communication. In the case study, we extend it to a multi-user scenario and summarize three trade-offs of the method. Finally, we conduct a qualitative analysis to assess the impact of three factors that have been overlooked in terrain-based UAV deployment.

Figures (7)

• Figure 1: Schematic diagram of UAV--aided terrain construction principles.
• Figure 2: UAV-aided terrain construction and parade crowd tracking.
• Figure 3: Schematic diagram of user distribution and UAV-user relative position.
• Figure 4: LoS probability at different elevation and azimuth angles.
• Figure 5: Coverage probability with different UAV densities.