UWB NLOS/LOS classification using hybrid quantum convolutional neural networks
Seon-Geun Jeong, Quang-Vinh Do, Hae-Ji Hwang, Mikio Hasegawa, Hiroo Sekiya, and Won-Joo Hwang
2023 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp.1-2, Oct. 2023. [pdf document]

<Abstract>

Ultra-wide-band (UWB) technology has emerged as a promising solution for providing accurate indoor positioning capabilities. However, indoor environments have various obstacles, which induce significant signal propagation effects. To avoid this issue, UWB signal classification is essential for improving positioning accuracy. This paper proposes a hybrid quantum convolutional neural network (HQCNN) inspired by convolutional neural networks for UWB none-line-of-sight/line-of-sight signal classification. The result shows that the HQCNN outperforms benchmarks.