Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
| Autor: | Masahiro Yukawa, Stefan Valentin, Slawomir Stanczak, Renato L. G. Cavalcante, Martin Kasparick |
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| Přispěvatelé: | Publica |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Networks and Communications Computer science Iterative method Aerospace Engineering Machine Learning (stat.ML) Multikernel 02 engineering and technology computer.software_genre Machine Learning (cs.LG) Computer Science - Networking and Internet Architecture 0203 mechanical engineering Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Online algorithm Block (data storage) Networking and Internet Architecture (cs.NI) Adaptive projected subgradient method 020302 automobile design & engineering 020206 networking & telecommunications Adaptive filter Kernel (statistics) Automotive Engineering Compressibility Data mining computer Data compression |
| Zdroj: | IEEE Transactions on Vehicular Technology. 65:5461-5473 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2015.2453391 |
| Popis: | 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 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 which is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast, 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. Comment: IEEE Transactions on Vehicular Technology; revised and extended version with new simulation scenario |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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