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Channel Modeling Aided Dataset Generation For AI-Enabled CSI Feedback: Advances, Challenges, and Solutions

Li, Yupeng, Li, Gang, Wen, Zirui, Han, Shuangfeng, Gao, Shijian, Liu, Guangyi, Wang, Jiangzhou (2024) Channel Modeling Aided Dataset Generation For AI-Enabled CSI Feedback: Advances, Challenges, and Solutions. IEEE Communications Standards Magazine, 8 (4). pp. 72-78. ISSN 2471-2833. (doi:10.1109/mcomstd.0001.2300053) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:108518)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1109/mcomstd.0001.2300053

Abstract

The AI-enabled autoencoder has demonstrated great potential in channel state information (CSI) feedback in frequency division duplex (FDD) multiple input multiple output (MIMO) systems. However, this method completely changes the existing feedback strategies, making it impractical to deploy in recent years. To address this issue, this article proposes a channel modeling aided data augmentation method based on a limited number of field channel data. Specifically, the user equipment (UE) extracts the primary stochastic parameters of the field channel data and transmits them to the base station (BS). The BS then updates the typical TR 38.901 model parameters with the extracted parameters. In this way, the updated channel model is used to generate the dataset. This strategy comprehensively considers the dataset collection, model generalization, model monitoring, and so on. Simulations verify that our proposed strategy can significantly improve performance compared to the benchmarks.

Item Type: Article
DOI/Identification number: 10.1109/mcomstd.0001.2300053
Uncontrolled keywords: Stochastic processes, Benchmark testing, Frequency conversion, Data augmentation, Data models, Data mining, Channel models, Monitoring, Channel state information, Communication standards
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: National Natural Science Foundation of China (https://ror.org/01h0zpd94)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 24 Jan 2025 13:42 UTC
Last Modified: 27 Jan 2025 15:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/108518 (The current URI for this page, for reference purposes)

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