2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSS-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
SessionSS-9: Contactless and Wireless Sensing for Smart Environments
LocationGather.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
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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.