Data-Driven 3D Placement of UAV Base Stations for Arbitrarily Distributed Crowds

Lai, Chuan-Chi; Wang, Lichun; Han, Zhu

doi:10.1109/globecom38437.2019.9014210

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…Besides, Kalantari et al optimize the 3D placement of multiple UAVBSs to find the minimum number of UAVBSs so that all users are served [19]. Lai et al discuss employing multiple UAVBSs cooperating with the terrestrial BSs [31]. The data-…”

Section: Related Workmentioning

confidence: 99%

3D Deployment of Unmanned Aerial Vehicle‐Base Station Assisting Ground‐Base Station

Hayajneh

Bani-Hani

Shakhatreh

et al. 2021

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs), also named as drones, have become a modern model to provide a quick wireless communication infrastructure. They have been used when conventional base stations’ capacity is suffering in some extreme cases such as congestion inside the cell or a special event. This paper proposes an efficient three-dimension (3D) placement of a single UAV-assisted wireless network in such cases. Our proposed model assists the ground base station (GBS) using the UAV to serve arbitrary distributed users considering the impact of the obstacle blockage over the well-known air-to-ground (A2G) path model. This work is aimed at optimizing the percentage of available bandwidth that must be provided to the UAV in order to maximize the number of served users. In addition, it finds the 3D placement of the UAV base station (UAVBS) that maximizes the number of served users, each with maximum quality-of-service (QoS). The exhaustive search and particle swarm optimization (PSO) algorithms are used to find the problem’s solution.

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Section: Related Workmentioning

confidence: 99%

3D Deployment of Unmanned Aerial Vehicle‐Base Station Assisting Ground‐Base Station

Hayajneh

Bani-Hani

Shakhatreh

et al. 2021

Wireless Communications and Mobile Computing

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“…UAV BSs deployed with the help of ML algorithms can provide a reliable service to users, despite the user pattern variation. Different strategies of ML, such as bio-inspired algorithms, unsupervised, and reinforcement learning (RL), have already been considered for optimal positioning of UAV BS in [22,23]. In [22], authors implemented a three dimensional UAV positioning with K-means clustering algorithm, an unsupervised ML algorithm that groups UEs to neighbouring cluster heads.…”

Section: Related Workmentioning

confidence: 99%

“…Different strategies of ML, such as bio-inspired algorithms, unsupervised, and reinforcement learning (RL), have already been considered for optimal positioning of UAV BS in [22,23]. In [22], authors implemented a three dimensional UAV positioning with K-means clustering algorithm, an unsupervised ML algorithm that groups UEs to neighbouring cluster heads. Another strategy is to use device-to-device communication to expand the coverage of the UAV BS using clustering algorithms [23].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization

Gopi

Magarini

2021

Electronics

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The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

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“…For example, given two 3D data, d 1 = [11,5,7] and d 2 = [15, 5,10]. We can say that d 1 dominates d 2 which is denoted as…”

Section: A Preliminarymentioning

confidence: 99%

Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments

Lai,

Liu,

Chen

et al. 2020

Preprint

Self Cite

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With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. To solve the above problems, edge computing has become a promising solution. In this paper, we propose a new algorithm for processing probabilistic skyline queries over uncertain data streams in an edge computing environment. We use the concept of a second skyline set to filter data that is unlikely to be the result of the skyline. Besides, the edge server only sends the information needed to update the global analysis results on the cloud server, which will greatly reduce the amount of data transmitted over the network. The results show that our proposed method not only reduces the response time by more than 50% compared with the brute force method on two-dimensional data but also maintains the leading processing speed on high-dimensional data.

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Data-Driven 3D Placement of UAV Base Stations for Arbitrarily Distributed Crowds

Cited by 16 publications

References 11 publications

3D Deployment of Unmanned Aerial Vehicle‐Base Station Assisting Ground‐Base Station

3D Deployment of Unmanned Aerial Vehicle‐Base Station Assisting Ground‐Base Station

Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization

Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments

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