Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI. This work extends traditional prediction approaches by focusing on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. Such a comprehensive framework is essential for addressing the dynamic nature of mobility environments within intelligent transportation systems, necessitating the capture of both temporal and spatial dependencies across diverse domains. To address this complexity, we propose a novel context-conditioned spatiotemporal predictive learning method. This method leverages causal convolutional long short-term memory (CA-ConvLSTM) to effectively capture dependencies within 4D CSI data, and incorporates context-conditioned attention mechanisms to enhance the efficiency of spatiotemporal memory updates. Additionally, we introduce an adaptive meta-learning scheme tailored for recurrent networks to mitigate the issue of accumulative prediction errors. We validate the proposed method through empirical studies conducted across three different geometric configurations and mobility scenarios. Our results demonstrate that the proposed approach outperforms existing state-of-the-art predictive models, achieving superior performance across various geometries. Moreover, we show that the meta-learning framework significantly enhances the performance of recurrent-based predictive models in highly challenging cross-geometry settings, thus highlighting its robustness and adaptability.

翻译：实现可靠的多维车对车（V2V）信道状态信息（CSI）预测对于优化依赖瞬时CSI的下游任务既具挑战性又至关重要。本研究通过聚焦于四维（4D）CSI（包括时间、带宽及天线（发射端与接收端）空间的预测）扩展了传统预测方法。此综合框架对于应对智能交通系统中移动环境的动态特性至关重要，需要捕捉跨不同领域的时空依赖性。为应对此复杂性，我们提出一种新颖的基于上下文条件的时空预测学习方法。该方法利用因果卷积长短期记忆（CA-ConvLSTM）有效捕捉4D CSI数据中的依赖性，并结合上下文条件注意力机制以提升时空记忆更新的效率。此外，我们引入一种专为循环网络设计的自适应元学习方案，以缓解累积预测误差问题。我们通过在三种不同几何配置与移动场景下进行的实证研究验证了所提方法。结果表明，所提方法优于现有最先进的预测模型，在各种几何场景下均实现了卓越性能。此外，我们证明元学习框架在极具挑战性的跨几何设置中显著提升了基于循环的预测模型的性能，从而突显了其鲁棒性与适应性。