| Paper ID | SS-9.6 |
| Paper Title |
Deep Convolutional Gaussian Processes for mmWave Outdoor Localization |
| Authors |
Xuyu Wang, Mohini Patil, California State University, Sacramento, United States; Chao Yang, Shiwen Mao, Auburn University, United States; Palak Anilkumar Patel, California State University, Sacramento, United States |
| Session | SS-9: Contactless and Wireless Sensing for Smart Environments |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Contactless and Wireless Sensing for Smart Environments |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Millimeter Wave (mmWave) communications, as a core technique of 5G, can be leveraged for outdoor localization because of its large bandwidth and massive antenna array. Fingerprinting based mmWave outdoor localization methods using deep learning are highly suitable for non-line-of-sight (NLOS) environments. In this paper, we propose a deep convolutional Gaussian process (DCGP) based regression approach to achieve high robustness for fingerprinting-based mmWave outdoor localization, which exploits the convolutional structure for deep Gaussian process to allow uncertainty estimation on location predictions. Specially, we present a system architecture of mmWave based outdoor localization, including beamforming image construction and DCGP training, where DCGP model can effectively learn the location features from mmWave beamforming images. Our experimental results show that the proposed DCGP method can achieve higher outdoor localization accuracy than a CNN-based baseline method. |
