Large Multimodal Model-Based Environment-Aware Beam Management

Beam management is an essential operation of next-generation (xG) wireless networks to compensate severe signal attenuation and ensure reliable communications over millimeter wave (mmWave) and terahertz (THz) bands. The primary objective of beam management is to determine beam directions that are properly aligned with the signal propagation paths. Conventional beam management techniques typically rely on geometric channel parameters such as angles, delays, and path gains. However, due to their lack of contextual awareness of the surrounding environment, these techniques fall short in handling the piecewise continuous changes in the geometric channel parameters caused by sudden obstructions or variations in scatterers. In this paper, we propose a novel beam management framework that utilizes environmental information extracted from sensor data (e.g., images) and pilot measurements to optimize beam directions. The main idea of the proposed scheme is to utilize visual channel parameters, including the positions of user equipment (UE), reflection points, and scatterers, which provide a direct visualization of the propagation environment. By tracking the visual channel parameters, dynamic changes in scatterers and propagation paths can be monitored over time. To analyze multimodal data and track these parameters, we exploit large multimodal model (LMM), a generative artificial intelligence (AI) model specialized in extracting correlated features across multimodal data and generating subsequent data. Simulation results show that the proposed scheme can accurately track the visual channel parameters and enhance data rate.