Large Language Models Empower Multimodal Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is considered as a key candidate technology for the sixth-generation (6G) wireless networks. Notably, an integration of multimodal sensing information within ISAC systems promises an improvement for communication performance. Nevertheless, traditional methods for ISAC systems are typically designed to handle unimodal data, making it challenging to effectively process and integrate semantically complex multimodal information. Moreover, they are usually customized for specific types of data or tasks, leading to poor generalization ability. Multi-modal large language models (MLLMs), which are trained on massive multimodal datasets and possess large parameter scales, are expected to be power-ful tools to address the above issues. In this article, we first introduce an MLLM-enabled ISAC system to achieve enhanced communication and sensing performance. We begin with the introduction of the fundamental principles of ISAC and MLLMs. Moreover, we present the overall system and the corresponding opportunities to be achieved. Furthermore, this article provides a case study to demonstrate the superior performance of MLLMs in handling the beam prediction task within ISAC systems. Finally, we discuss several research challenges and potential directions for future research.