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Spatial Channel State Information Prediction With Generative AI: Toward Holographic Communication and Digital Radio Twin
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DOI：10.1109/MNET.2024.3421940.png)
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Abstract
En 中文
As the deployment of 5G technology matures, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies. A pivot to these radio technologies is the effective management of large-scale antenna arrays, which aims to construct valid spatial streams to maximize system throughput. Traditional management methods predominantly rely on user feedback to adapt to dynamic wireless channels. However, a more promising approach lies in the prediction of spatial channel state information (spatial-CSI), which is a channel characterization that consists of all robust line-of-sight (LoS) and non-line-of-sight (NLoS) paths between the transmitter (Tx) and receiver (Rx), with three-dimensional (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. Recent advances in hardware and neural networks make it possible to predict such spatial-CSI using precise environmental information, and further explores the possibility of holographic communication, which implies complete control over every aspect of the radio waves. This paper presents a preliminary exploration of using generative artificial intelligence (AI) to accurately model the environment particularly for radio simulations and identify valid paths within it for real-time spatial-CSI prediction. Our validation project demonstrates promising results, highlighting the potential of this approach in driving forward the evolution of 6G wireless communication technologies.
Keywords:
Antenna arrays
Wireless communication
Real-time systems
Precoding
Array signal processing
Streams
6G mobile communication
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