Machine-Learning-Assisted Scenario Classification Using Large-Scale Fading Characteristics and Geographic Information
Author:
Affiliation:
1. Southeast University,State Key Laboratory of mmWave,Nanjing,China
2. China Mobile Group Design Institute Co., Ltd.,Beijing,China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9880588/9880614/09880790.pdf?arnumber=9880790
Reference17 articles.
1. Supporting Enhanced Vehicle-to-Everything Services by LTE Release 15 Systems
2. Artificial intelligence enabled radio propagation for communicationsPart II: Scenario identifi-cation and channel modeling;huang;IEEE Trans Antennas Propag,2022
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1. Channel Scenario Extensions, Identifications, and Adaptive Modeling for 6G Wireless Communications;IEEE Internet of Things Journal;2023
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