Abstract
Due to the broadcast nature of radio transmission, both authorized and unauthorized users can access the network, which leads to the increasingly prominent security problems of wireless network. At the same time, it is more difficult to detect and identify users in wireless network environment due to the influence of noise. In this paper, the performance of energy detection (ED), matched filtering (MF) and K-nearest neighbor algorithm (KNN) are analyzed under different noise and uncertain noise separately. The Gaussian noise, α-stable distribution noise and Laplace distribution noise models are simulated respectively under the different uncertainty of noise when the false alarm probability is 0.01. The results show that the performance of the detectors is significantly affected by different noise models. In any case, the detection probability of KNN algorithm is the highest; the performance of MF is much better than ED under different noise models; KNN is not sensitive to noise uncertainty; MF has better performance on noise uncertainty which makes ED performance decline fleetly.
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This work was funded by the National Natural Science Foundation of China (Grant Nos. 61461052, 11564044, 61863035).
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Wang, X., Yang, J., Yu, T., Li, R., Huang, M. (2021). Performance Evaluation of Energy Detection, Matched Filtering and KNN Under Different Noise Models. In: Deze, Z., Huang, H., Hou, R., Rho, S., Chilamkurti, N. (eds) Big Data Technologies and Applications. BDTA WiCON 2020 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-72802-1_10
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