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AI for CSI Prediction in 5G-Advanced and Beyond

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arxiv 2504.12571 v1 pith:F2IIGZRG submitted 2025-04-17 eess.SP

classification eess.SP
keywords predictioncommunicationg-advancedresearchsystemswirelessaccuracyacknowledged
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Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing

    cs.LG 2025-10 conditional novelty 5.0 of 10

    A hybrid CNN-ShuffleNet-Transformer model claims the best accuracy-efficiency trade-off on a new 3,060-scenario CSI prediction benchmark, but FDD gains and robustness claims rest on point estimates without error bars.

  2. Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks

    cs.LG 2025-06 accept novelty 3.0 of 10

    A position paper synthesises 3GPP Release 18/19 AI/ML discussions and recommends hybrid, modular machine-learning directions for the 6G air interface.

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