Multi-Dimensional Wireless Tomography Using Tensor-Based Compressed Sensing

作者：Matsuda Takahiro^*; Yokota Kengo; Takemoto Kazushi; Hara Shinsuke; Ono Fumie; Takizawa Kenichi; Miura Ryu

来源：Wireless Personal Communications, 2017, 96(3): 3361-3384.

DOI：10.1007/s11277-017-4061-2

• Wireless tomography
• Compressed sensing
• Tensor

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. With simulation experiments, we reveal that the tensor-based compressed sensing has a potential for highly accurate estimation as compared with the vector-based compressed sensing. In order to show the possibility of the wireless tomography schemes in practical environments, we also show an experimental result in an anechoic chamber.

• 出版日期2017-10