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arXiv · 1407.2394

Multi-Dimensional Wireless Tomography with Tensor-Based Compressed Sensing

Kazushi Takemoto·Takahiro Matsuda·Shinsuke Hara·Kenichi Takizawa·Fumie Ono·Ryu Miura
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Abstract

Wireless tomography is a technique for inferring a physical environment within a monitored region by analyzing RF signals traversed across the region. In this paper, we consider wireless tomography in a two and higher dimensionally structured monitored region, and propose a multi-dimensional wireless tomography scheme based on compressed sensing to estimate a spatial distribution of shadowing loss in the monitored region. In order to estimate the spatial distribution, we consider two compressed sensing frameworks: vector-based compressed sensing and tensor-based compressed sensing. When the shadowing loss has a high spatial correlation in the monitored region, the spatial distribution has a sparsity in its frequency domain. Existing wireless tomography schemes are based on the vector-based compressed sensing and estimates the distribution by utilizing the sparsity. On the other hand, the proposed scheme is based on the tensor-based compressed sensing, which estimates the distribution by utilizing its low-rank property. We reveal that the tensor-based compressed sensing has a potential for highly accurate estimation as compared with the vector-based compressed sensing.

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Kazushi Takemoto, Takahiro Matsuda, Shinsuke Hara, Kenichi Takizawa, Fumie Ono, Ryu Miura. 2014-07-09. Multi-Dimensional Wireless Tomography with Tensor-Based Compressed Sensing. https://arxiv.org/abs/1407.2394

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