Indoor Millimeter Wave Localization Using Multiple Self-Supervised Tiny Neural Networks
Author:
Affiliation:
1. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
2. NEC Laboratories Europe, Heidelberg, Germany
Funder
European Commission’s Horizon 2020 Framework Program under the Marie Skłodowska-Curie Action MINTS
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/4234/10527213/10466634.pdf?arnumber=10466634
Reference15 articles.
1. A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications
2. Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget
3. Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments
4. Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach
5. Comparison of Neural Network Training Functions for RSSI Based Indoor Localization Problem in WSN
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