Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction

TL;DR

This paper introduces a novel 4D CSI prediction method for V2V channels that combines causal convolutional LSTM, attention mechanisms, and meta-learning to improve accuracy and robustness in dynamic transportation environments.

Contribution

It proposes a context-conditioned spatiotemporal learning framework with CA-ConvLSTM and meta-learning, advancing the state-of-the-art in 4D V2V channel prediction.

Findings

Outperforms existing models across multiple geometries.

Meta-learning improves performance in cross-geometry scenarios.

Achieves higher accuracy in dynamic mobility environments.

Abstract

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.