Spatial Channel State Information Prediction with Generative AI: Towards
  Holographic Communication and Digital Radio Twin
Lihao Zhang Haijian Sun Yong Zeng Rose Qingyang Hu
As 5G technology becomes increasingly established, the anticipation for 6G is
growing, which promises to deliver faster and more reliable wireless
connections via cutting-edge radio technologies. However, efficient management
method of the large-scale antenna arrays deployed by those radio technologies
is crucial. Traditional management methods are mainly reactive, usually based
on feedback from users to adapt to the dynamic wireless channel. However, a
more promising approach lies in the prediction of spatial channel state
information (spatial-CSI), which is an all-inclusive channel characterization
and consists of all the feasible line-of-sight (LoS) and non-line-of-sight
(NLoS) paths between the transmitter (Tx) and receiver (Rx), with the
three-dimension (3D) trajectory, attenuation, phase shift, delay, and
polarization of each path. Advances in hardware and neural networks make it
possible to predict such spatial-CSI using precise environmental information,
and further look into the possibility of holographic communication, which
implies complete control over every aspect of the radio waves emitted. Based on
the integration of holographic communication and digital twin, we proposed a
new framework, digital radio twin, which takes advantages from both the digital
world and deterministic control over radio waves, supporting a wide range of
high-level applications. As a preliminary attempt towards this visionary
direction, in this paper, we explore the use of generative artificial
intelligence (AI) to pinpoint the valid paths in a given environment,
demonstrating promising results, and highlighting the potential of this
approach in driving forward the evolution of 6G wireless communication
technologies.