Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information

Martin Kasparick, Renato L.G. Cavalcante, Stefan Valentin, Sławomir Stańczak, Masahiro Yukawa

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method (APSM) and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data that is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast and robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.

Original languageEnglish
Article number7152980
Pages (from-to)5461-5473
Number of pages13
JournalIEEE Transactions on Vehicular Technology
Volume65
Issue number7
DOIs
Publication statusPublished - 2016 Jul

Keywords

  • Adaptive filters
  • Kernel-based filtering machine learning
  • coverage estimation
  • mobile communications

ASJC Scopus subject areas

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

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