Indoor Mobile Coverage Problem Using UAVs

Hazim Shakhatreh, Abdallah Khreishah, Issa Khalil

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks are not operational due to natural disasters. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. Prior studies on UAV-based wireless coverage typically consider an air-to-ground path loss model, which assumes that the users are outdoor and located on a 2-D plane. In this paper, we propose using UAVs to provide wireless coverage for indoor users inside a high-rise building. First, we present realistic outdoor-indoor path loss models and describe the tradeoff introduced by these models. Then, we study the problem of efficient placement of a single UAV, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is generally difficult to solve. To that end, we consider three cases of practical interest and provide efficient solutions to the formulated problem under these cases. Then, we study the problem of minimizing the number of UAVs required to provide wireless coverage to the high-rise building and prove that this problem is NP-complete. Due to the intractability of the problem, we use clustering to minimize the number of UAVs required to cover the indoor users. In our proposed algorithm, we check if the maximum transmit power of a UAV is sufficient to cover each cluster. If not, the number of clusters is incremented by one, and the problem is solved again. In the uniform split method, we split the building into $k$ regular structures and utilize $k$ UAVs to provide wireless coverage for indoor users regardless of user distribution. We demonstrate through simulations that the method that splits the building into regular structures requires 80% more number of UAVs relative to our proposed algorithm.

Original languageEnglish (US)
Article number8352733
Pages (from-to)3837-3848
Number of pages12
JournalIEEE Systems Journal
Volume12
Issue number4
DOIs
StatePublished - Dec 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Gradient descent (GD) algorithm
  • k-means clustering
  • outdoor-to-indoor path loss model
  • particle swarm optimization (PSO)
  • unmanned aerial vehicles (UAVs)

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