A Machine-Learning Approach for the Exemplar Extraction of mmWave Industrial Wireless Channels
Author:
Affiliation:
1. Communications Technology Laboratory, NIST, Gaithersburg, MD, USA
2. Communications Technology Laboratory, NIST, Boulder, CO, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
https://ieeexplore.ieee.org/ielam/9552935/9687502/9795178-aam.pdf
Reference57 articles.
1. Low-Latency Heterogeneous Networks with Millimeter-Wave Communications
2. Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks
3. Temporal Exemplar Channels In High-Multipath Environments
4. Fingerprint Extraction and Classification of Wireless Channels Based on Deep Convolutional Neural Networks
5. Wireless channel feature extraction via GMM and CNN in the tomographic channel model
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