Vision-Aided Channel Prediction Based on Image Segmentation at Street Intersection Scenarios
Journal:
arXiv
Published Date:
Jan 27, 2025
Abstract
Intelligent vehicular communication with vehicle road collaboration
capability is a key technology enabled by 6G, and the integration of various
visual sensors on vehicles and infrastructures plays a crucial role. Moreover,
accurate channel prediction is foundational to realizing intelligent vehicular
communication. Traditional methods are still limited by the inability to
balance accuracy and operability based on substantial spectrum resource
consumption and highly refined description of environment. Therefore,
leveraging out-of-band information introduced by visual sensors provides a new
solution and is increasingly applied across various communication tasks. In
this paper, we propose a computer vision (CV)-based prediction model for
vehicular communications, realizing accurate channel characterization
prediction including path loss, Rice K-factor and delay spread based on image
segmentation. First, we conduct extensive vehicle-to-infrastructure measurement
campaigns, collecting channel and visual data from various street intersection
scenarios. The image-channel dataset is generated after a series of data
post-processing steps. Image data consists of individual segmentation of target
user using YOLOv8 network. Subsequently, established dataset is used to train
and test prediction network ResNet-32, where segmented images serve as input of
network, and various channel characteristics are treated as labels or target
outputs of network. Finally, self-validation and cross-validation experiments
are performed. The results indicate that models trained with segmented images
achieve high prediction accuracy and remarkable generalization performance
across different streets and target users. The model proposed in this paper
offers novel solutions for achieving intelligent channel
prediction in vehicular communications.
