Optimization of Deployable Base Stations with Guaranteed QoE in Disaster Scenarios

Junbo Wang, Song Guo, Zixue Cheng, Peng Li, Jie Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Reconstructing emergency communication networks (ECNs) quickly after a disaster occurs is critical so that people can share information and confirm their safety. In recent studies, deployable base stations (DBSs) have demonstrated their ability to reconstruct an ECN. However, considering limited resources, it is impossible to deploy DBSs in the whole disaster area. The above shortage can be covered by deploying small-cell networks (i.e., low-power transmission base stations) in areas with high communication demand, e.g., in refuges. Considering the above two-tier ECN, in this paper, we study its performance and optimization issue with the objective of minimizing the number/density of DBSs while guaranteeing acceptable coverage probabilities for both communication tiers. The majority of current research focuses on scenarios where the base stations follow a homogeneous Poisson point process of coverage probability. It is difficult to transfer the results to other applications, e.g., when communication resources are shared, such as by refugees following a disaster. In such cases, the distribution of users is closer to that of a Poisson cluster process. We then investigate the optimization method to minimize the number/density of DBSs. We used Monte Carlo methods with various parameter choices to evaluate the results and to determine the accuracy of our evaluation.

Original languageEnglish
Article number7748571
Pages (from-to)6536-6552
Number of pages17
JournalIEEE Transactions on Vehicular Technology
Volume66
Issue number7
DOIs
Publication statusPublished - Jul 2017

Keywords

  • Anti-disaster network
  • association probability
  • coverage probability
  • heterogeneous emergency communication networks (ECNs)
  • Poisson cluster process (PCP)

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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