Fingerprinting-Based Indoor Localization With Hybrid Quantum-Deep Neural Network

被引:0
作者
Eberechukwu, Paulson N. [1 ]
Jeong, Minsoo [1 ]
Park, Hyunwoo [1 ]
Choi, Sang Won [2 ]
Kim, Sunwoo [1 ]
机构
[1] Hanyang Univ, Dept Elect Engn, Seoul 04763, South Korea
[2] Kyonggi Univ, Dept Elect Engn, Suwon 16227, South Korea
基金
新加坡国家研究基金会;
关键词
Indoor localization; fingerprinting; quantum computing; QNN; DNN;
D O I
10.1109/ACCESS.2023.3341972
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of quantum computing (QC) and deep neural networks (DNN). The strengths of QC, which accelerates the training process and enables efficient handling of complex data representations through quantum superposition and entanglement, were combined with DNN, known for its ability to extract meaningful features and learn complex patterns from data. The proposed model can be trained using small datasets, reducing the need for extensive data, particularly useful in indoor localization, where data collection can be time-consuming and resource-intensive. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments and comparisons with existing state-of-the-art methods. The results demonstrate that the H-QDNN model significantly improves indoor localization accuracy compared to traditional techniques. Additionally, we provide insights into the factors contributing to enhanced performance, such as the quantum-inspired algorithms utilized and the integration of mixed fingerprints.
引用
收藏
页码:142276 / 142291
页数:16
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