3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting

Abstract

Dispatching unmanned aerial vehicles (UAVs) to harvest sensing-data from distributed sensors is expected to significantly improve the data collection efficiency in conventional wireless sensor networks (WSNs). In this paper, we consider a UAV-enabled WSN where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic ('S' shape) function with respect to the 3D UAV trajectory by using the data regression method. Then the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its nonconvexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV's horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Last, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

“…The more accurate approximation of the expected throughput over general UAV-ground channels and the corresponding UAV trajectory optimization is nontrivial, which requires further investigation (see, e.g. [81]).…”

“…While the above works on communication-trajectory codesign mostly focused on 2D trajectory with fixed UAV altitude, more research efforts are needed for 3D trajectorycommunication co-design to fully exploit the 3D UAV mobility, especially in dense urban environment [81]. To this end, more sophisticated channel models and performance metrics as discussed in Section II-A and Section II-D need to be used.…”

Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels

“…Note that different from the prior joint design of UAV trajectory and communication scheduling (see e.g., [4], [14], [17]), our proposed method decouples the design into the UAV path optimization in the offline phase, followed by the real-time adjustment of the UAV flying speeds and communication scheduling in the online phase. This is motivated by the fact that the path optimization requires solving a time-consuming non-linear optimization problem (as will be…”

“…The vision of Internet-of-Drones (IoD) has spurred intensive enthusiasm in recent years on deploying unmanned aerial vehicles (UAVs) (or Drones) to automate a proliferation of applications, such as aerial inspection, photography, packet delivery, remote sensing, and so on [2], [3]. Particularly for wireless communications, the unique features of UAVs such as high mobility, controllably maneuver as well as LoS-dominant air-ground channels have incentivized both academia and industry to integrate them into the conventional terrestrial wireless networks for enhancing their coverage and throughput, leading to various new applications, such as UAV-assisted terrestrial communications [3]- [8], cellular-connected UAVs [9]- [11], UAV-enabled mobile relaying [12], [13], UAV-enabled wireless sensor networks (WSNs) [14]- [17], to name a few. Specifically, for the UAV-enabled WSNs that utilize UAVs as mobile data collectors to directly receive data from spatially-separated SNs, one key problem is to design the UAV trajectory in the three-dimensional (3D) space for maximizing data harvesting throughput or minimizing data collection time.…”

“…Such characteristics can be captured by the angle-dependent Rician fading channel model [18], where the Rician factor generally increases with the elevation angle between the UAV and its served ground node. Based on this model, 3D UAV trajectory has been designed in [17] for maximizing the data harvesting throughput in UAV-enabled WSNs. In contrast, if the UAV flies at a relatively low altitude, the shadowing effect becomes more significant, due to which the signal propagation between the UAV and a ground node can be occasionally blocked by buildings, where the likelihood of blockage in general depends on the relative position between the UAV and ground node, as well as the distributions of building density and height.…”