Millimeter-wave (mmWave) and terahertz (THz) communication systems require large antenna arrays and use narrow directive beams to ensure sufficient receive signal power. However, selecting the optimal beams for these large antenna arrays incurs a significant beam training overhead, making it challenging to support applications involving high mobility. In recent years, machine learning (ML) solutions have shown promising results in reducing the beam training overhead by utilizing various sensing modalities such as GPS position and RGB images. However, the existing approaches are mainly limited to scenarios with only a single object of interest present in the wireless environment and focus only on co-located sensing, where all the sensors are installed at the communication terminal. This brings key challenges such as the limited sensing coverage compared to the coverage of the communication system and the difficulty in handling non-line-of-sight scenarios. To overcome these limitations, our paper proposes the deployment of multiple distributed sensing nodes, each equipped with an RGB camera. These nodes focus on extracting environmental semantics from the captured RGB images. The semantic data, rather than the raw images, are then transmitted to the basestation. This strategy significantly alleviates the overhead associated with the data storage and transmission of the raw images. Furthermore, semantic communication enhances the system's adaptability and responsiveness to dynamic environments, allowing for prioritization and transmission of contextually relevant information. Experimental results on the DeepSense 6G dataset demonstrate the effectiveness of the proposed solution in reducing the sensing data transmission overhead while accurately predicting the optimal beams in realistic communication environments.

翻译：毫米波（mmWave）和太赫兹（THz）通信系统需要大型天线阵列，并采用窄定向波束以确保足够的接收信号功率。然而，为这些大型天线阵列选择最优波束会带来显著的波束训练开销，使得支持高移动性应用面临挑战。近年来，机器学习（ML）解决方案通过利用各种感知模态（如GPS位置和RGB图像），在降低波束训练开销方面展现出良好前景。然而，现有方法主要局限于无线环境中仅存在单个感兴趣物体的场景，并仅关注共置感知，即所有传感器均安装在通信终端上。这带来了关键挑战，例如感知覆盖范围相对于通信系统覆盖范围有限，以及难以应对非视距场景。为克服这些局限，本文提出部署多个分布式感知节点，每个节点配备RGB摄像头。这些节点专注于从捕获的RGB图像中提取环境语义。随后，将语义数据（而非原始图像）传输至基站。该策略显著缓解了原始图像数据存储与传输带来的开销。此外，语义通信增强了系统对动态环境的适应性和响应能力，允许对上下文相关信息进行优先级排序和传输。在DeepSense 6G数据集上的实验结果表明，所提方案在降低感知数据传输开销的同时，能在真实通信环境中准确预测最优波束，具有有效性。