Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the Wireless V2I Channels Using Deep Neural Networks   For high data rate wireless communication systems, developing an efficient
channel estimation approach is extremely vital for channel detection and signal
recovery. With the trend of high-mobility wireless communications between
vehicles and vehicles-to-infrastructure (V2I), V2I communications pose
additional challenges to obtaining real-time channel measurements. Deep
learning (DL) techniques, in this context, offer learning ability and
optimization capability that can approximate many kinds of functions. In this
paper, we develop a DL-based channel prediction method to estimate channel
responses for V2I communications. We have demonstrated how fast neural networks
can learn V2I channel properties and the changing trend. The network is trained
with a series of channel responses and known pilots, which then speculates the
next channel response based on the acquired knowledge. The predicted channel is
then used to evaluate the system performance.

20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc