Abstract
With the fast growth of the wireless spectrum demand, people have been focusing on utilizing indoor white spaces. In the past few years, several indoor white space exploration methods have been proposed. These methods focus on the utilization of spatial and spectral correlations of white spaces. However, these correlations change over time. In this paper, we perform indoor white space synchronous measurement to demonstrate the volatility of white spaces. Then, we propose a DCN (Deep Convolutional Network)-based method to capture the statistical dependencies among the features and combinatorial features extracted from white spaces, which are not limited to spatial or spectral features, and construct the white space availability map. After demonstrating the instability of spectral and spatial correlations, we scale our DCN models to a time-agnostic model. We conduct real-world experiments to evaluate our system. The evaluation results show that our time-specific DCN model and time-agnostic model outperforms the state-of-the-art method.
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Acknowledgements
This work was supported in part by National Key R&D Program of China No. 2019YFB2102200, in part by China NSF grant No. 62025204, 62072303, 61972252, and 61972254, 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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Xiang, Y., Zheng, Z., Qu, Y., Chen, G. (2021). Scaling DCN Models for Indoor White Space Exploration. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_41
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DOI: https://doi.org/10.1007/978-3-030-86130-8_41
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