Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges

Notice: This work has been submitted to the IEEE for possible publication. Abstract--Large language models (LLMs) and large multi-modal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this article, we provide an overview of task-oriented autonomous communications with LLMs/LMMs, focusing on multimodal sensing integration, adaptive reconfiguration, and prompt/fine-tuning strategies for wireless tasks. We demonstrate the framework through three case studies: LMM-based traffic control, LLM-based robot scheduling, and LMM-based environment-aware channel estimation. From experimental results, we show that the proposed LLM/LMM-aided autonomous systems significantly outperform conventional and discriminative deep learning (DL) model-based techniques, maintaining robustness under dynamic objectives, varying input parameters, and heterogeneous multimodal conditions where conventional static optimization degrades. Driven by the huge success of ChatGPT, large language models (LLMs) have gained widespread attention, reshaping various fields by solving problems with zero-shot or few-shot prompting. Recently, large multimodal models (LMMs) extend this capability by embracing various modalities such as images, videos, and audio. These models can handle changing objectives and input variations using diverse multimodal observations.