Few-Shot Learning with Multimodal Fusion for Efficient Cloud-Edge Collaborative Communication

As demand for high-capacity, low-latency communication rises, mmWave systems are essential for enabling ultra-high-speed transmission in fifth-generation mobile communication technology (5G) and upcoming 6G networks, especially in dynamic, data-scarce environments. However, deploying mmWave systems in dynamic environments presents significant challenges, especially in beam selection, where limited training data and environmental variability hinder optimal performance. In such scenarios, computation offloading has emerged as a key enabler, allowing computationally intensive tasks to be shifted from resource-constrained edge devices to powerful cloud servers, thereby reducing latency and optimizing resource utilization. This paper introduces a novel cloud-edge collaborative approach integrating few-shot learning (FSL) with multimodal fusion to address these challenges. By leveraging data from diverse modalities-such as red-green-blue (RGB) images, radar signals, and light detection and ranging (LiDAR)-within a cloud-edge architecture, the proposed framework effectively captures spatiotemporal features, enabling efficient and accurate beam selection with minimal data requirements. The cloud server is tasked with computationally intensive training, while the edge node focuses on real-time inference, ensuring low-latency decision making. Experimental evaluations confirm the model's robustness, achieving high beam selection accuracy under one-shot and five-shot conditions while reducing computational overhead. This study highlights the potential of combining cloud-edge collaboration with FSL and multimodal fusion for next-generation wireless networks, paving the way for scalable, intelligent, and adaptive mmWave communication systems.