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

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

研究成果: Article査読

43 被引用数 (Scopus)


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.

ジャーナルIEEE Transactions on Vehicular Technology
出版ステータスPublished - 2016 7月

ASJC Scopus subject areas

  • 自動車工学
  • 航空宇宙工学
  • 電子工学および電気工学
  • 応用数学


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