Cellular Coverage Map as a Voronoi Diagram

Portela, J.N.; Alencar, Marcelo S.

doi:10.14209/jcis.2008.3

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…Many interpolation methods have also been proposed to construct the coverage maps. Portela and Alencar [22] estimated the KPIs of points by the data provided by the nearest sampling points, which leaded to a Voronoi diagram [28], to enable the divisions of Voronoi polygons where the KPIs of arbitrary points were totally determined by the data of the polygon center points. Martin et al [23] utilized the Inverse Distance Weighting (IDW) technique to construct the coverage maps with the crowd-sourced cellular coverage data.…”

Section: Related Workmentioning

confidence: 99%

“…Numerical model methods can estimate the KPIs of all sampling points which are intensive and even inaccessible for vehicle in DT methods by numerical calculations, such as model calculations [18]- [21] and interpolation methods [22]- [27], to construct more refined coverage maps. However, the computation speeds of such algorithms are slow due to the large number of the sampling points.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

et al. 2019

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Wireless cellular communications lead to huge demands for estimating and visualizing the data about Quality of Service (QoS) for mobile network operators in the 5th Generation (5G) networks. Constructing a coverage map is an important step to visualize global information about QoS. Inspired by the characteristic of the base station, we present an adaptive triangulation method to divide the region of interest into triangles. Then, we propose a novel area-wise Multi-criteria Triangulation-induced Interpolation (MTI) algorithm which utilizes the linear interpolation to estimate the key performance indicators of the QoS inside a triangle with the known values of its three vertexes, to construct the coverage maps and provide the closedform solution of the covered region for the multi-criteria problems. We check the accuracy and the efficiency of the MTI algorithm both in 19-cells network scenario and in real big city scenario. The experiment results manifest that the MTI algorithm shows a good performance in constructing the coverage maps and it is significantly lower-cost and higher-efficiency than the traditional point-wise algorithms. INDEX TERMS Wireless cellular communication, coverage map, triangulation method, linear interpolation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

et al. 2019

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“…Grid-based approaches [9][10][11][12][13][14][15][16][17][18] mainly divide the area into sub-areas to solve dynamic area coverage problems. In [9], Choset presented a strategy dividing the space into cell regions covered with back-andforth motions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A mixed-integer linear programming (MILP) model has been used to determine a coverage path planning for visiting all generated nodes in the shortest amount of time. Voronoi partitions have been used for optimum area coverage task [14][15][16]. The connection is established on a cell which is selected by a closest-point search on decomposed area.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Aydemir¹,

Çetin²

2023

Computer Systems Science and Engineering

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Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents' states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.

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“…Centroidal Voronoi and its applications are discussed in [13], a primary dynamic algorithm to construct Voronoi diagram is proposed in [14], and generalization of Voronoi diagram and algorithms are extensively discussed in [15]. The area coverage of cellular networks with respect to Voronoi partitioning is studied in [16]. The authors in [17] study BS location optimization using the concept of node function.…”

Section: Introductionmentioning

confidence: 99%

Cellular Network Planning under Variable QoS Requirements Using Voronoi Algorithm

Abedi

Wichman

2020

2020 IEEE Wireless Communications and Networking Conference (WCNC)

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We study the optimization of base station (BS) localization in cellular networks with non-uniform service demand distribution taking into account a variety of server-side and user-side requirements. To this end, we propose a power Voronoi algorithm (PVA) to obtain uniform traffic volume shares per cell while simultaneously meeting the user-side and serverside requirements of the heterogeneous devices. We describe a numerical method to solve the optimization problem by updating the weights associated to different cells in the PVA. Then, we take into account minimum signal to interference plus noise ratio at all devices as a user-side requirement. It is observed that BSs are concentrated in the areas with condensed demand and high SINR requirements. To show the advantages of the proposed method, the results are compared for different SINR requirement distributions giving insight on the performance of the algorithm.

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Cellular Coverage Map as a Voronoi Diagram

Cited by 10 publications

References 15 publications

Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

Multi-Criteria Coverage Map Construction Based on Adaptive Triangulation-Induced Interpolation for Cellular Networks

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Cellular Network Planning under Variable QoS Requirements Using Voronoi Algorithm

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