Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artificial intelligence enabled radio propagation for
  communications-Part II: Scenario identification and channel modeling   This two-part paper investigates the application of artificial intelligence
(AI) and in particular machine learning (ML) to the study of wireless
propagation channels. In Part I, we introduced AI and ML as well as provided a
comprehensive survey on ML enabled channel characterization and antenna-channel
optimization, and in this part (Part II) we review state-of-the-art literature
on scenario identification and channel modeling here. In particular, the key
ideas of ML for scenario identification and channel modeling/prediction are
presented, and the widely used ML methods for propagation scenario
identification and channel modeling and prediction are analyzed and compared.
Based on the state-of-art, the future challenges of AI/ML-based channel data
processing techniques are given as well.

4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an