Large Multimodal Model-Aided Scheduling for 6G Autonomous Communications

TL;DR

This paper introduces a large multimodal model-based scheduling method for 6G autonomous communications that predicts future channel conditions using visual and pilot data, enabling proactive resource allocation.

Contribution

It presents a novel LMM-based scheduling approach that leverages multimodal inputs to predict channel parameters and improve scheduling in dynamic 6G environments.

Findings

Achieves over 30% throughput gain compared to traditional methods.

Effectively predicts future channel states using multimodal data.

Enhances resource scheduling accuracy in rapidly changing channels.

Abstract

Recently, large language models (LLMs) have gained significant attention for their ability to generate fast and accurate answer to the given query. These models have evolved into large multimodal models (LMMs), which can interpret and analyze multimodal inputs such as images and text. With the exponential growth of AI functionalities in autonomous devices, the central unit (CU), a digital processing unit performing AI inference, needs to handle LMMs to effectively control these devices. To ensure seamless command delivery to devices, the CU must perform the scheduling, which involves resource block (RB) allocation for data transmission and modulation and coding scheme (MCS) index selection based on the channel conditions. This task is challenging in many practical environments in 6G, where even small user movement can cause abrupt channel changes. In this paper, we propose a novel LMM-based scheduling technique to address this challenge. Our key idea is to leverage LMM to predict future channel parameters (e.g., distance, angles, and path gain) by analyzing the visual sensing information as well as pilot signals. By exploiting LMMs to predict the presence of reliable path and geometric information of users from the visual sensing information, and then combining these with past channel states from pilot signals, we can accurately predict future channel parameters. Using these predictions, we can preemptively make channel-aware scheduling decisions. From the numerical evaluations, we show that the proposed technique achieves more than 30% throughput gain over the conventional scheduling techniques.