Digital Twin of Channel: Diffusion Model for Sensing-Assisted Statistical Channel State Information Generation

With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the conc...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 24; H. 5; S. 3805 - 3821
Hauptverfasser: Gong, Xinrui, Liu, Xiaofeng, Lu, An-An, Gao, Xiqi, Xia, Xiang-Gen, Wang, Cheng-Xiang, You, Xiaohu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
6G mobile communication
Accuracy
Artificial intelligence
Cellular communication
Communication
deep generative model
diffusion model
Diffusion models
Digital twin
Digital twins
Fingerprint recognition
integrated sensing and communication
Location awareness
Neural networks
Noise
Sensors
Simulation
statistical channel information generation
Training
Virtual reality
ISSN:1536-1276, 1558-2248
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Zusammenfassung:With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the concept of Digital Twin of Channel (DToC). Specifically, we regard user terminal (UT) positions as physical objects, and statistical channel state information (CSI) as virtual digital objects. Observing the change trend of UTs' statistical CSI caused by the changes of UT's physical position enables predictive analytics for subsequent communication tasks. Then, we establish the relationship between physical and virtual digital objects using a Diffusion Model (DM) to achieve the DToC. Indeed, the DM can generate the desired objects by gradually denoising from noisy data using neural networks. Furthermore, we propose a conditional DM utilizing UTs' positions, which completes the task of generating the corresponding statistical CSI under known user-specific position conditions, thus mapping UT positions to statistical CSI. Simulation results demonstrate that our DToC framework outperforms previous statistical CSI estimation methods. Without the need of pilots, our method can simultaneously generate statistical CSIs from a large number of UTs' positions, achieving satisfactory results.
Bibliographie:ObjectType-Article-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3542429