Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems

Recent breakthroughs in multimodal large language models (MLLMs) have endowed AI systems with unified perception, reasoning and natural-language interaction across text, image and video streams. Meanwhile, Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, safety-critical missions that demand rapid situational understanding and autonomous adaptation. This paper explores potential solutions for integrating MLLMs with UAV swarms to enhance the intelligence and adaptability across diverse tasks. Specifically, we first outline the fundamental architectures and functions of UAVs and MLLMs. Then, we analyze how MLLMs can enhance the UAV system performance in terms of target detection, autonomous navigation, and multi-agent coordination, while exploring solutions for integrating MLLMs into UAV systems. Next, we propose a practical case study focused on the forest fire fighting. To fully reveal the capabilities of the proposed framework, human-machine interaction, swarm task planning, fire assessment, and task execution are investigated. Finally, we discuss the challenges and future research directions for the MLLMs-enabled UAV swarm. An experiment illustration video could be found online at https://youtu.be/zwnB9ZSa5A4.

Beam Training and Alignment for RIS-Assisted Millimeter Wave Systems:State of the Art and Beyond

Reconfigurable intelligent surface (RIS) has recently emerged as a promising paradigm for future cellular networks. Specifically, due to its capability in reshaping the propagation environment, RIS was introduced to address the blockage issue in millimeter Wave (mmWave) or even Terahertz (THz) communications. The deployment of RIS, however, complicates the system architecture and poses a significant challenge for beam training (BT)/ beam alignment (BA), a process that is required to establish a reliable link between the transmitter and the receiver. In this article, we first review several state-of-the-art beam training solutions for RIS-assisted mmWave systems and discuss their respective advantages and limitations. We also present a new multi-directional BT method, which can achieve a decent BA performance with only a small amount of training overhead. Finally, we outline several important open issues in BT for RIS-assisted mmWave systems.