Applying Deep-Learning-Based Computer Vision to Wireless Communications:
Methodologies,Opportunities, and Challenges
Abstract
Deep learning (DL) has seen great success in the computer vision (CV)
field, and related techniques have been used in security, healthcare,
remote sensing, and many other fields. As a parallel development, visual
data has become universal in daily life, easily generated by ubiquitous
low-cost cameras. Therefore, exploring DL-based CV may yield useful
information about objects, such as their number, locations,
distribution, motion, etc. Intuitively, DL-based CV can also facilitate
and improve the designs of wireless communications, especially in
dynamic network scenarios. However, so far, such work is rare in the
literature. The primary purpose of this article, then, is to introduce
ideas about applying DL-based CV in wireless communications to bring
some novel degrees of freedom to both theoretical research and
engineering applications. To illustrate how DL-based CV can be applied
in wireless communications, an example of using a DL-based CV with a
millimeter-wave (mmWave) system is given to realize optimal mmWave
multiple-input and multiple-output (MIMO) beamforming in mobile
scenarios. In this example, we propose a framework to predict future
beam indices from previously observed beam indices and images of street
views using ResNet, 3-dimensional ResNext, and a long short-term memory
network. The experimental results show that our frameworks achieve much
higher accuracy than the baseline method, and that visual data can
significantly improve the performance of the MIMO beamforming system.
Finally, we discuss the opportunities and challenges of applying
DL-based CV in wireless communications.