The paper introduces RIS-CoCsiNet, a novel deep learning-based framework for efficient CSI feedback in RIS-assisted multi-user systems. By leveraging correlations among nearby UEs, the approach reduces feedback overhead through shared and individual data categorization. The integration of LSTM modules for multiple antenna UEs and magnitude-dependent phase feedback strategies further enhance the system's performance.
The International Telecommunication Union's approval of 6G networks has spurred advancements in wireless communication techniques like reconfigurable intelligent surfaces (RISs) and AI. The paper focuses on optimizing channel state information (CSI) feedback in RIS-supported systems to meet the demands of 6G scenarios.
Key challenges in traditional CSI feedback methods are addressed through innovative deep learning approaches that exploit correlations among proximate UEs and introduce novel cooperative mechanisms. The proposed framework showcases significant improvements in simulation results from diverse channel datasets.
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by Jiajia Guo,X... om arxiv.org 03-12-2024
https://arxiv.org/pdf/2003.03303.pdfDiepere vragen