Abstract
Exploring white spaces in the indoor environment has been recognized as a promising way to satisfy the rapid growth of the wireless spectrum demand. Although a few indoor white space exploration systems have been proposed in the past few years, they mainly focused on exploring white spaces at a small set of candidate locations. However, what we need are the white space availabilities at arbitrary indoor locations instead of only those at the candidate locations. In this paper, we first perform an indoor TV spectra measurement to study the characteristics of indoor white spaces in a fine-grained way. Then, we propose a Fine-gRained Indoor white Space Estimation mechanism, called FRISE, which could accurately estimate the white space availabilities at arbitrary indoor locations. FRISE mainly consists of a method to determine the positions of candidate locations and an accurate spatial interpolation algorithm. Furthermore, we evaluate the performance of FRISE based on real-world measured data. The evaluation results show that FRISE outperforms the existing methods in estimating white spaces at arbitrary indoor locations.
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Acknowledgement
This work was supported in part by National Key R&D Program of China No. 2019YFB2102200, in part by China NSF grant No. 62025204, 61972252, 61972254, 61672348, and 61672353, in part by Joint Scientific Research Foundation of the State Education Ministry No. 6141A02033702, in part by Alibaba Group through Alibaba Innovation Research Program, and in part by Tencent Rhino Bird Key Research Project. The opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies or the government.
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Wu, F. et al. (2020). Towards Fine-Grained Indoor White Space Sensing. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_4
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