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As 5G technology becomes increasingly established, the anticipation for 6G isgrowing, which promises to deliver faster and more reliable wirelessconnections via cutting-edge radio technologies. However, efficient managementmethod of the large-scale antenna arrays deployed by those radio technologiesis crucial. Traditional management methods are mainly reactive, usually basedon feedback from users to adapt to the dynamic wireless channel. However, amore promising approach lies in the prediction of spatial channel stateinformation (spatial-CSI), which is an all-inclusive channel characterizationand consists of all the feasible line-of-sight (LoS) and non-line-of-sight(NLoS) paths between the transmitter (Tx) and receiver (Rx), with thethree-dimension (3D) trajectory, attenuation, phase shift, delay, andpolarization of each path. Advances in hardware and neural networks make itpossible to predict such spatial-CSI using precise environmental information,and further look into the possibility of holographic communication, whichimplies complete control over every aspect of the radio waves emitted. Based onthe integration of holographic communication and digital twin, we proposed anew framework, digital radio twin, which takes advantages from both the digitalworld and deterministic control over radio waves, supporting a wide range ofhigh-level applications. As a preliminary attempt towards this visionarydirection, in this paper, we explore the use of generative artificialintelligence (AI) to pinpoint the valid paths in a given environment,demonstrating promising results, and highlighting the potential of thisapproach in driving forward the evolution of 6G wireless communicationtechnologies.

Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin