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

TL;DR

This work tackles reliable multidimensional V2V channel prediction by introducing a context-conditioned spatiotemporal predictor built on CA-ConvLSTM, enriched with temporal and spatiotemporal attentions. The authors address accumulative prediction error (APE) through a meta-learning framework with pseudo labels and an adaptive teacher, and validate performance across three distinct geometries with real measurement data. Key contributions include (i) a context-conditioned memory update mechanism, (ii) a meta-pseudo-label optimization strategy, and (iii) a minor adaptive refinement that enhances cross-geometry robustness. The results show notable improvements over ConvLSTM and ST-ConvLSTM baselines, with the meta-learning approach particularly boosting cross-geometry generalization, thereby offering a robust path toward practical, high-reliability V2V channel prediction in dynamic 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.

Figures (9)

• Figure 1: Overall framework of the proposed method. We use the memory attentions as contextual focus. For example, When processing an input sequence, attention mechanisms enable the model to concentrate on various parts of the sequence in a context-sensitive manner. In our model, the temporal context allows the network to learn sequence dependencies in the delay domain, while the spatio-temporal context provides focus in the angular domain.
• Figure 2: A comparison of different meta learning strategies.
• Figure 3: Measurement campaigns.
• Figure 4: CSI Prediction performance in three different scenarios, with the upper row displaying MSE results and the lower row showing MAE results.
• Figure 5: The cumulative distribution function of MSEs for all predictive algorithms in three scenarios (same-geo).

Theorems & Definitions (3)

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