Three-Dimensional-Map-Based Trajectory Design in UAV-Aided Wireless Localization Systems

Esrafilian, Omid; Gangula, Rajeev; Gesbert, David

doi:10.1109/jiot.2020.3021611

Cited by 28 publications

(17 citation statements)

References 35 publications

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“…In contrast, initializing λ i with a small value will provide enough degree of freedom for UAV positioning to obtain a good starting point. Then, by gradually increasing the value of λ i via (27), the upper bound given by Lagrangian problem (15) can be gradually strengthened. Ultimately, constraint ( 14) is satisfied, which means that the UAV is positioned in a region without any building blockage.…”

Section: Updating Lagrangian Multipliersmentioning

confidence: 99%

“…In [26], a geometric analysis method to detect the blockage was proposed, and then a greedy UE scheduling algorithm was performed to avoid the blockage and enhance the spectral efficiency of a multi-UAV communication system. In [27], UAV trajectory was designed for outdoor UE localization based on received signal strength measurements, where geographic information was utilized to distinguish LoS/NLoS environments.…”

Section: Introductionmentioning

confidence: 99%

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Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Yi¹,

Li²,

Zhu³

et al. 2021

Preprint

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<div>In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we fifirst model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confifirm that the proposed solution signifificantly outperforms two benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.</div>

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Section: Updating Lagrangian Multipliersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Yi¹,

Li²,

Zhu³

et al. 2021

Preprint

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show abstract

“…The problem of simultaneous wireless node localization and channel learning has been studied in previously [13]. In this section, we propose a new approach of model-free node localization by leveraging the 3D map of the environment.…”

Section: Model-aided Deep Q-learningmentioning

confidence: 99%

“…In this section, we propose a new approach of model-free node localization by leveraging the 3D map of the environment. Akin to [13], a LoS/NLoS segmented radio channel is assumed. However, in contrast to [13], our goal here is to estimate the radio channel using a model-free method while localizing the ground nodes.…”

Section: Model-aided Deep Q-learningmentioning

confidence: 99%

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Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks

Esrafilian,

Bayerlein,

Gesbert

2021

Preprint

Self Cite

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Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in the context of cellular or Internet of Things (IoT) connectivity. DRL solutions offer the advantage of on-the-go learning hence relying on very little prior contextual information. A corresponding drawback however lies in the need for many learning episodes which severely restricts the applicability of such approach in real-world time-and energy-constrained missions.Here, we propose a model-aided deep Q-learning approach that, in contrast to previous work, considerably reduces the need for extensive training data samples, while still achieving the overarching goal of DRL, i.e to guide a battery-limited UAV towards an efficient data harvesting trajectory, without prior knowledge of wireless channel characteristics and limited knowledge of wireless node locations. The key idea consists in using a small subset of nodes as anchors (i.e. with known location) and learning a model of the propagation environment while implicitly estimating the positions of regular nodes. Interaction with the model allows us to train a deep Q-network (DQN) to approximate the optimal UAV control policy. We show that in comparison with standard DRL approaches, the proposed model-aided approach requires at least one order of magnitude less training data samples to reach identical data collection performance, hence offering a first step towards making DRL a viable solution to the problem.

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Robust BDS/5G integrated positioning based on resilient observation model

Zhang

Yang

Zeng

et al. 2023

Advances in Space Research

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Three-Dimensional-Map-Based Trajectory Design in UAV-Aided Wireless Localization Systems

Cited by 28 publications

References 35 publications

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks

Robust BDS/5G integrated positioning based on resilient observation model

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