  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.
