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Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid Precoding by Luo, Hao; Alkhateeb, Ahmed is a scholarly article available to read on EtoBox.

What is Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid Precoding about?

Compressive sensing is a promising solution for the channel estimation in multiple-input multiple-output (MIMO) systems with large antenna arrays and constrained hardware. Utilizing site-specific channel data from real-world systems, deep learning can be employed to learn the compressive sensing measurement vectors with minimum redundancy, thereby focusing sensing power on promising spatial directions of the channel. Collecting real-world channel data, however, is challenging due to the high overhead resulting from the large number of antennas and hardware constraints. In this paper, we propose leveraging a site-specific digital twin to generate synthetic channel data, which shares a similar distribution with real-world data. The synthetic data is then used to train the deep learning models for learning measurement vectors and hybrid precoder/combiner design in an end-to-end manner. We further propose a model refinement approach to fine-tune the model pre-trained on the digital twin data with a small amount of real-world data. The evaluation results show that, by training the model on the digital twin data, the learned measurement vectors can be efficiently adapted to the environme

Author

Luo, Hao; Alkhateeb, Ahmed

Published

2024

Language

EN