Vision Image Aided Near-Field Beam Training for Internet of Vehicle Systems
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所属单位：School of Computer Science and Information Engineering, Hefei University of Technology
发表刊物：2024 IEEE International Conference on Communications Workshops
项目来源：the National Natural Science Foundation of China under Grants 61971176, 62371180 and 62171474，Anhui
关键字：Vision Image, Near-field, Beam Training
摘要：In this paper, we develop a novel beam training scheme for extremely large-scale multiple-input-multiple-output (XL-MIMO) system by exploiting the visual image information. Different from the conventional beam training schemes that consumes a large number of in-band (time/frequency) resources, the proposed scheme only leverages the out-of-band (vision image) information, which can efficiently reduce the training overhead. Specifically, we proposed a vision image-aided beam training cascaded framework integrating YOLOv5 and ResNet18 networks, where the YOLOv5 uses the object detection technique to extract the size and location information of the mobile vehicles (MVs) and the ResNet18 based the extracted information infers the optimal beam index without occupying in-band overhead. The simulation results demonstrate that the proposed vision image aided beam training scheme outperforms the benchmark scheme.
合写作者：Xueqing Huang,Haiyang Zhang,Kunyang Sun,Caihong Kai,Shiwen He
第一作者：Wei Huang
论文类型：论文集
学科门类：工学
文献类型：C
页面范围：390-395
是否译文：否
发表时间：2024-06-09
收录刊物：EI