Cellular Coverage-Aware Path Planning for UAVs

Bast, Sibren De; Vinogradov, Evgenii; Pollin, Sofie

doi:10.1109/spawc.2019.8815469

Cited by 23 publications

(14 citation statements)

References 10 publications

(10 reference statements)

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“…The Al-Hourani path loss model is described in (14), where FSPL is the free space path loss model from the Friss equation.  is the normal distribution for the excess path loss with a mean of , and standard deviation as expressed in (15), where and are frequency and environment-dependent variables and is the elevation angle between the ground and the aerial nodes. This model is suitable for urban environments with building representation.…”

Section: Path Loss and Channel Modellingmentioning

confidence: 99%

“…In order to totally exploit the potential of the UAV-BS networks, several challenges need to be addressed, such as the proper location of the UAV-BSs and the optimisation of the radio resource allocation to cope with the limit imposed by the access, and the BH network capacity [1,13,14,15,16]. In the literature, effort has been put in the design of UAV-BSs Radio Access Network (RAN).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of flying caching servers in UAV-BS based realistic environment

Castellanos

Vallero

Deruyck

et al. 2021

Vehicular Communications

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Section: Path Loss and Channel Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of flying caching servers in UAV-BS based realistic environment

Castellanos

Vallero

Deruyck

et al. 2021

Vehicular Communications

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“…In other words, the main difference between Q-learning and double Q-learning is to use two Q functions instead of one function. Moreover, at each iteration, we update only one of these two functions based on (10) or (11).…”

Section: Double Q-learning Fundamentalsmentioning

confidence: 99%

“…The trajectory optimization problem for the cellularconnected aerial vehicles has been investigated for different scenarios in a number of recent studies [2], [7]- [11]. In [2], the trajectory of an aerial vehicle is optimized with the objective of minimizing the travel time of the aerial vehicle while a minimum signal to noise ratio (SNR) constraint needs to be satisfied at all time instances.…”

Section: Introductionmentioning

confidence: 99%

A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint

Khamidehi¹,

Sousa²

2020

Preprint

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This paper studies the trajectory optimization problem for an aerial vehicle with the mission of flying between a pair of given initial and final locations. The objective is to minimize the travel time of the aerial vehicle ensuring that the communication connectivity constraint required for the safe operation of the aerial vehicle is satisfied. We consider two different criteria for the connectivity constraint of the aerial vehicle which leads to two different scenarios. In the first scenario, we assume that the maximum continuous time duration that the aerial vehicle is out of the coverage of the ground base stations (GBSs) is limited to a given threshold. In the second scenario, however, we assume that the total time periods that the aerial vehicle is not covered by the GBSs is restricted. Based on these two constraints, we formulate two trajectory optimization problems. To solve these non-convex problems, we use an approach based on the double Q-learning method which is a model-free reinforcement learning technique and unlike the existing algorithms does not need perfect knowledge of the environment. Moreover, in contrast to the well-known Q-learning technique, our double Q-learning algorithm does not suffer from the over-estimation issue. Simulation results show that although our algorithm does not require prior information of the environment, it works well and shows near optimal performance.

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“…The Optimal positioning and trajectory planning of UAVs is discussed in [3,7,16] for improving the connectivity and quality of cellular-enabled UAVs. The channel gain map-based approach is used in [7] for learning the UAV trajectory and for maximizing the communication throughput.…”

Section: Introductionmentioning

confidence: 99%

Quality-aware trajectory planning of cellular connected UAVs

Sheikh

Riaz

Jameel

et al. 2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

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The use of Unmanned Aerial Vehicles (UAVs) is becoming common in our daily lives and cellular networks are effective in providing support services to UAVs for long-range applications. The main target of this paper is to propose a modified form of well-known graph search methods i.e., Dijkstra and A-star also known as A* algorithm, for quality-aware trajectory planning of the UAV. The aerial quality map of the propagation environment is used as an input for UAV trajectory planning, and the quality metric considered for this work is Signal to Interference plus Noise Ratio (SINR). The UAV trajectory is quantified in terms of three performance metrics i.e., path length, Quality Outage Ratio (QOR), and maximum Quality Outage Duration (QOD). The proposed path planning algorithm aims at achieving a trade-off between the path length and other quality metrics of the UAV trajectory. The simulations are performed using an agreed 3GPP macro cell LOS scenario for UAVs in MATLAB. Simulation results illustrate that the proposed algorithm significantly improves the QOR by slightly increasing the path length compared with the naive shortest path. Similarly, the outage avoidance path achieves high QOR at the expense of large path length, and our proposed method finds a compromise and provides an optimal quality-aware path.

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Cellular Coverage-Aware Path Planning for UAVs

Cited by 23 publications

References 10 publications

Evaluation of flying caching servers in UAV-BS based realistic environment

Evaluation of flying caching servers in UAV-BS based realistic environment

A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint

Quality-aware trajectory planning of cellular connected UAVs

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