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

By Changsheng You, Rui Zhang. IEEE Transactions on Wireless Communications, 2019

362 citations recorded by the source corpus.

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 non-convexity. 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. Finally, 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.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/50bed0baf83b76345a346f4152dad0310127186c

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Citations in the corpus

UAV-Assisted Data Collection for Internet of Things: A Survey (2022, methodology): “In [148], You and Zhang initially optimize the communication scheduling of sensors and 3D trajectory of UAV in Rician Fading Channel to improve the data collection efﬁciency.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “While the above works on communication-trajectory co-design mostly focused on 2D trajectory with ﬁxed UAV altitude, more research efforts are needed for 3D trajectory-communication co-design to fully exploit the 3D UAV mobility, especially in dense urban environment [81].”
Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, neutral): “…placement/trajectories of AIRSs jointly with their passive beamforming and user association for maximizing the average rates of all users under the practical UAV-ground channel models [167] (such as the elevation-angle dependent Rician fading channel [177] and the probabilistic LoS channel [178]).”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, background): “The horizontal position and altitude of a UAV can be flexibly adjusted to improve the channel quality, which provides new DoFs for the optimization of wireless communication systems [347], [348].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, neutral): “…Mixture models [105], [106] • Autoencoders [107] • Generative adversarial network [108], [109] • Support-vector machine [110] • Logistic regression [111] • Deep neural network [107], [112], [113] • Deep Q-learning [114]–[116] • Policy gradient [117]–[119] • Actor-critic [120], [121] • Deep…”