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AI-Enabled Data-Driven Channel Modeling for Future Communications
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DOI：10.1109/MCOM.019.2300072.png)
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Abstract
En 中文
Wireless channel modeling plays an essential role in the design of wireless communication networks. The future integrated network with various applications and extended-spectrum especially needs an accurate channel model as the cornertone. However, with the expansion of scenarios, frequencies, and user requirements, classical channel modeling methods face many limitations, and new approaches must be explored. Artificial intelligence (AI) has become one of the key technologies in the evolution of wireless communication systems. Recent research has applied AI technology to predicting channel characteristics, such as path loss. This article presents an AI-enabled channel modeling framework for future communication networks, which aims to establish a nonlinear model between environmental information and channel characteristics. Then, we expound on the proposed framework's architecture and features and analyzes some key technologies involved. Finally, we point out technical challenges.
Keywords:
Adaptation models
Mathematical models
Data models
Complexity theory
Wireless communication
Data mining
Frequency measurement
Channel estimation
Artificial intelligence
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