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.

翻译：近年来，大型语言模型因其能够快速准确生成给定查询的答案而受到广泛关注。这些模型已发展为大型多模态模型，能够解析和分析图像与文本等多模态输入。随着自主设备中人工智能功能的指数级增长，作为执行AI推理的数字处理单元的中央单元需要处理LMM以有效控制这些设备。为确保向设备无缝传递指令，中央单元必须执行调度，这包括为数据传输分配资源块以及基于信道条件选择调制与编码方案索引。在6G的许多实际应用场景中，该任务极具挑战性，因为即使用户的微小移动也可能导致信道突变。本文提出一种基于LMM的新型调度技术以应对这一挑战。我们的核心思想是利用LMM通过分析视觉感知信息与导频信号来预测未来信道参数（如距离、角度和路径增益）。通过借助LMM从视觉感知信息中预测可靠路径的存在与用户几何信息，并将其与导频信号的历史信道状态相结合，我们能够精准预测未来信道参数。利用这些预测结果，我们可以预先制定信道感知的调度决策。数值评估表明，所提技术相比传统调度方案可实现超过30%的吞吐量增益。