Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks

Abstract

The evolution toward sixth-generation (6G) wireless communications introduces unprecedented demands for ultra-reliable low-latency communication (URLLC) in vehicle-to-everything (V2X) networks, where fast-moving vehicles and the use of high-frequency bands make it challenging to acquire the channel state information to maintain high-quality connectivity. Traditional methods for estimating channel coefficients rely on pilot symbols transmitted during each coherence interval; however, the combination of high mobility and high frequencies significantly reduces the coherence times, necessitating substantial bandwidth for pilot transmission. Consequently, these conventional approaches are becoming inadequate, potentially causing inefficient channel estimation and degraded throughput in such dynamic environments. This paper presents a novel multimodal collaborative perception framework for dynamic channel prediction in 6G V2X networks, integrating LiDAR data to enhance the accuracy and robustness of channel predictions. Our approach synergizes information from connected agents and infrastructure, enabling a more comprehensive understanding of the dynamic vehicular environment. A key innovation in our framework is the prediction horizon optimization (PHO) component, which dynamically adjusts the prediction interval based on real-time evaluations of channel conditions, ensuring that predictions remain relevant and accurate. Extensive simulations using the MVX (Multimodal V2X) high-fidelity co-simulation framework demonstrate the effectiveness of our solution. Compared to baseline methods—namely, a classical LS-LMMSE approach and a wireless-based model that solely relies on channel measurements—our framework achieves up to a 30.82% reduction in mean squared error (MSE) and a 32.76% increase in goodput. These gains underscore the efficiency of the PHO component in reducing prediction errors, maintaining low bit error rates, and meeting the stringent requirements of 6G V2X communications. Consequently, our framework establishes a new benchmark for AI-driven channel prediction in next-generation wireless networks, particularly in challenging urban and rural scenarios.