Journal
IEEE ACCESS
Volume 4, Issue -, Pages 5520-5530Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2604360
Keywords
Position measurement; millimeter wave technology; artificial neural networks; ranging; device to device; 5G
Categories
Funding
- Nature Science Foundation of China [61301139, 61671482]
- Nature Science Foundation of Shandong Province [ZR2014FL014, ZR2014FM017]
- Fundamental Research Funds for the Central Universities [16CX02046A]
- Project of Basic Research Application of Qingdao City [14-2-4-83-jch]
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Current generation mobile wireless communication networks are not suitable for real-time positioning applications because timing information is not readily available. Fifth generation (5G) cellular networks provide device to device real-time communications which can be used for real-time positioning. Millimeter-wave (mmWave) transmission is regarded as a key technology in 5G networks. In this paper, several 73-GHz mmWave waveforms are investigated. A new threshold selection algorithm for energy detector-based ranging is proposed which employs a dynamic threshold based on an artificial neural network. The positioning performance using this algorithm with mmWave waveforms is investigated.
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