Abstract
Highly directional millimeter-wave (mmW) connectivity - especially in industry-grade scenarios with complex and unpredictable device mobility - requires a certain degree of structural redundancy in the network, which can be provided by utilizing multi-connectivity mechanisms. To lower the coordination complexity and overhead of tracking multiple directional beams, mmW networks can retrieve and leverage timely positioning information. In this paper, we develop a holistic framework for the co-design of networking and positioning in industrial 5G mmW deployments with multi-connectivity capabilities. In particular, we propose a flexible two-stage positioning solution - mindful of information uncertainty - that relies upon the 5G NR system design and can be seamlessly integrated into the mmW cellular infrastructure with reasonable overheads. We reproduce a typical 5G mmW network deployment featuring dissimilar device mobility patterns and assess the performance of the proposed architecture. In particular, we evaluate the precision of our positioning and base station orientation estimation methods as well as analyze the impact of the proposed scheme on the system-level performance. Our numerical results demonstrate that the proposed solution yields highly accurate position estimates and significantly improves the average network spectral efficiency.
Original language | English |
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Pages (from-to) | 15842-15856 |
Number of pages | 14 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 96 |
Issue number | 12 |
DOIs | |
Publication status | Published - 2020 |
Publication type | A1 Journal article-refereed |
Keywords
- 3GPP
- 5G mobile communication
- 5G New Radio
- Antenna arrays
- Antenna measurements
- Azimuth
- directionality
- Estimation
- industrial verticals
- mmW
- mobility
- multi-connectivity
- positioning and tracking
- Uncertainty
Publication forum classification
- Publication forum level 3
ASJC Scopus subject areas
- Automotive Engineering
- Aerospace Engineering
- Electrical and Electronic Engineering
- Applied Mathematics