Abstract
Wireless communication channels around 2.4 GHz are shared by a number of popular wireless protocols, such as WiFi, Bluetooth, Zigbee, implemented on off-the-shelf devices. The fast increasing number of internet-of-things (IoTs) devices introduce serious challenge on reliable communication due to the problem of cross-technology interference. While, the interference problem can be mitigated if the type of the interference source is known so that the sophisticated interference avoidance method can be facilitated to improve the communication quality. In this paper, we focus on the cross-technology interference problem in indoor environment. We propose to use Channel State Information (CSI) to detect and classify the type of the interference. According to our evaluation on dataset collected from real-world experiments, our proposed CSI-based approach achieved significant performance gain compared with existing RSSI-based approach when using different classification methods including Nearest Neighborhood (NN), Supportive Vector Machine (SVM) and Sparse Representation Classification (SRC).
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Acknowledgements
This work is partially supported by National Natural Science Foundation of China under Grant 61702132 and 61702133, Natural Science Foundation of Heilongjiang province under grant QC2017069 and QC2017071, the Fundamental Research Funds for the Central Universities under Grant HEUCFJ160601, the China Postdoctoral Science Foundation under Grant 166875 and Heilongjiang Postdoctoral Fundation under grant LBH-Z16042.
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Yang, Z., Wang, Y., Zhang, L., Shen, Y. (2018). Indoor Interference Classification Based on WiFi Channel State Information. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_11
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DOI: https://doi.org/10.1007/978-3-030-05345-1_11
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