  Vision-aided wireless communication is attracting increasing interest and
finding new use cases in various wireless communication applications. These
vision-aided communication frameworks leverage visual data captured, for
example, by cameras installed at the infrastructure or mobile devices to
construct some perception about the communication environment through the use
of deep learning and advances in computer vision and visual scene
understanding. Prior work has investigated various problems such as
vision-aided beam, blockage, and hand-off prediction in millimeter wave
(mmWave) systems and vision-aided covariance prediction in massive MIMO
systems. This prior work, however, has focused on scenarios with a single
object (user) in front of the camera. In this paper, we define the \textit{user
identification} task as a key enabler for realistic vision-aided communication
systems that can operate in crowded scenarios and support multi-user
applications. The objective of the user identification task is to identify the
target communication user from the other candidate objects (distractors) in the
visual scene. We develop machine learning models that process either one frame
or a sequence of frames of visual and wireless data to efficiently identify the
target user in the visual/communication environment. Using the large-scale
multi-modal sense and communication dataset, DeepSense 6G, which is based on
real-world measurements, we show that the developed approaches can successfully
identify the target users with more than 97$\%$ accuracy in realistic settings.
This paves the way for scaling the vision-aided wireless communication
applications to real-world scenarios and practical deployments.
