Detail publikace

Simulating UAV's Movement for Servicing User Groups with a Reference Point in Wireless Networks

KHAYROV, E. POLYAKOV, N. MEDVEDEVA, E. POKORNÝ, J. GAIDAMAKA, Y. HOŠEK, J.

Originální název

Simulating UAV's Movement for Servicing User Groups with a Reference Point in Wireless Networks

Anglický název

Simulating UAV's Movement for Servicing User Groups with a Reference Point in Wireless Networks

Jazyk

en

Originální abstrakt

Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and exible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones' mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.

Anglický abstrakt

Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and exible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones' mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.

Dokumenty

BibTex


@inproceedings{BUT164922,
  author="Jiří {Pokorný} and Jiří {Hošek}",
  title="Simulating UAV's Movement for Servicing User Groups with a Reference Point in Wireless Networks",
  annote="Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and exible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones' mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.",
  address="Springer, Cham",
  booktitle="Internet of Things, Smart Spaces, and Next Generation Networks and Systems",
  chapter="164922",
  doi="10.1007/978-3-030-65729-1_37",
  edition="Lecture Notes in Computer Science",
  howpublished="online",
  institution="Springer, Cham",
  year="2020",
  month="december",
  pages="415--425",
  publisher="Springer, Cham",
  type="conference paper"
}