Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Hyun Jong Yang, Hyunsoo Kim, Hyeonho Noh, Seungnyun Kim, Byonghyo Shim

TL;DR

The paper addresses the challenge of enabling task-oriented autonomy in wireless systems by introducing a CU-centric framework that leverages large language and multimodal models to fuse multimodal sensing data and drive end-to-end autonomous control. It details a design methodology including task specification, multimodal input compression, end-to-end orchestration, and lightweight fine-tuning and prompting techniques (e.g., LoRA, CoT) to adapt to dynamic environments. Three case studies—LMM-based V2X traffic control, environment-aware channel estimation, and dynamic robot scheduling—demonstrate substantial performance gains over traditional DL and heuristic baselines, validating the approach under varying objectives and conditions. The work highlights practical implications for 6G autonomous communications, suggesting that LLMs/LMMs can serve as holistic decision-makers and system optimizers when integrated with sensing, communication, and computation in a robust, scalable framework.

Abstract

Large language models (LLMs) and large multimodal 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.

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

TL;DR

The paper addresses the challenge of enabling task-oriented autonomy in wireless systems by introducing a CU-centric framework that leverages large language and multimodal models to fuse multimodal sensing data and drive end-to-end autonomous control. It details a design methodology including task specification, multimodal input compression, end-to-end orchestration, and lightweight fine-tuning and prompting techniques (e.g., LoRA, CoT) to adapt to dynamic environments. Three case studies—LMM-based V2X traffic control, environment-aware channel estimation, and dynamic robot scheduling—demonstrate substantial performance gains over traditional DL and heuristic baselines, validating the approach under varying objectives and conditions. The work highlights practical implications for 6G autonomous communications, suggesting that LLMs/LMMs can serve as holistic decision-makers and system optimizers when integrated with sensing, communication, and computation in a robust, scalable framework.

Abstract

Large language models (LLMs) and large multimodal 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.
Paper Structure (10 sections, 7 figures, 1 table)

This paper contains 10 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. LLM/LMM-based Wireless Systems
  3. AI/ML-Aided Wireless Systems
  4. LLM/LMM-Based Task-Oriented Communication Systems: Goals and Characteristics
  5. LLM/LMM-Based Wireless Systems: Design Methodology
  6. Case Studies and Performance Evaluation
  7. LMM-Aided V2X Communications for Vehicle Traffic Control
  8. LMM-Based Environment-Aware Channel Estimation
  9. Dynamically-Varying Robot Scheduling using Fine-Tuned LLM
  10. Discussion and Challenges

Figures (7)

  • Figure 1: LLM/LMM-based system design for task-oriented communications.
  • Figure 2: Design procedure of LLM/LMM-based task-oriented communications.
  • Figure 3: Illustration of traffic sign control using fine-tuned LMM.
  • Figure 4: Average vehicle speed as a function of the number of vehicles.
  • Figure 5: Illustration of LMM-based environment-aware channel estimation.
  • ...and 2 more figures