LLM4SP: Large Language Models for Scatterer Prediction via Synesthesia of Machines

Guided by Synesthesia of Machines (SoM), the nonlinear mapping relationship between sensory and communication information serves as a powerful tool to enhance both the accuracy and generalization of vehicle-to-vehicle (V2V) multi-modal intelligent channel modeling (MMICM) in intelligent transportation systems (ITSs). To explore the general mapping relationship between physical environment and electromagnetic space, a new intelligent sensing-communication integration dataset, named V2V-M3, is constructed for multiple scenarios in V2V communications with multiple frequency bands and multiple vehicular traffic densities (VTDs). Leveraging the strong representation and cross-modal inference capabilities of large language models (LLMs), a novel LLM-based method for Scatterer Prediction (LLM4SP) from light detection and ranging (LiDAR) point clouds is developed. To address the inherent and significant differences across multi-modal data, synergistically optimized four-module architecture, i.e., preprocessor, embedding, backbone, and output modules, are designed by considering the sensing/channel characteristics and electromagnetic propagation mechanism. On the basis of cross-modal representation alignment and positional encoding, the network of LLM4SP is fine-tuned to capture the general mapping relationship between LiDAR point clouds and scatterers. Simulation results demonstrate that the proposed LLM4SP achieves superior performance in full-sample and generalization testing, significantly outperforming small models across different frequency bands, scenarios, and VTDs.