  Integrated sensing and communications is a key enabler for the 6G wireless
communication systems. The multiple sensing modalities will allow the base
station to have a more accurate representation of the environment, leading to
context-aware communications. Some widely equipped sensors such as cameras and
RADAR sensors can provide some environmental perceptions. However, they are not
enough to generate precise environmental representations, especially in adverse
weather conditions. On the other hand, the LiDAR sensors provide more accurate
representations, however, their widespread adoption is hindered by their high
cost. This paper proposes a novel approach to enhance the wireless
communication systems by synthesizing LiDAR point clouds from images and RADAR
data. Specifically, it uses a multimodal transformer architecture and
pre-trained encoding models to enable an accurate LiDAR generation. The
proposed framework is evaluated on the DeepSense 6G dataset, which is a
real-world dataset curated for context-aware wireless applications. Our results
demonstrate the efficacy of the proposed approach in accurately generating
LiDAR point clouds. We achieve a modified mean squared error of 10.3931. Visual
examination of the images indicates that our model can successfully capture the
majority of structures present in the LiDAR point cloud for diverse
environments. This will enable the base stations to achieve more precise
environmental sensing. By integrating LiDAR synthesis with existing sensing
modalities, our method can enhance the performance of various wireless
applications, including beam and blockage prediction.
