AI 中文总结
针对XL-MIMO V2I通信近场波束训练开销高的问题，提出基于雷达-波束图学习的被动雷达辅助框架，经仿真验证可提升多项关键性能指标。
AI 中文摘要
极大多输入多输出（XL-MIMO）车到基础设施（V2I）系统中的近场波束训练，因大尺寸距离-角度码本及快速信道变化而产生高开销。本文提出一种基于雷达-波束图学习的被动雷达辅助近场波束预测框架，利用雷达观测与通信信号间的空间相关性，采用轻量级编码器-解码器卷积神经网络将雷达Bartlett谱映射至通信波束图，还引入高斯软监督以保留波束空间连续性。在同步Sionna射线追踪雷达-通信数据集上的仿真表明，所提方法可持续提升Top-k准确率、基于距离的准确率、波束损耗及频谱效率。
英文摘要
Near-field beam training in extremely large-scale multiple-input multiple-output (XL-MIMO) vehicle-to-infrastructure (V2I) systems incurs high overhead due to large range-angle codebooks and rapid channel variation. This paper proposes a passive radar-aided framework for near-field beam prediction based on radar-to-beam map learning. By exploiting the spatial correlation between radar observations and communication signals, the proposed method maps radar Bartlett spectra to communication beam maps using a lightweight encoder-decoder convolutional neural network. Gaussian soft supervision is further introduced to preserve beam-space continuity. Simulations on a synchronized Sionna ray tracing radar-communication dataset show that the proposed method consistently improves Top-k accuracy, distance-based accuracy, beam loss, and spectral efficiency.
