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Prompt-Enabled Large AI Models for CSI Feedback
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Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This study explores the mechanism through analyzing performance across diverse datasets, with findings suggesting that superior feedback performance stems from AI models' strong fitting capabilities and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt enabled large AI model (LAM) for CSI feedback. The LAM employs powerful transformer blocks and is trained on extensive datasets from various scenarios. Meanwhile, the channel distribution (environmental knowledge) -- represented as the mean of channel magnitude in the angular-delay domain -- is incorporated as a prompt within the decoder to further enhance reconstruction quality. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.
Forward citations
Cited by 7 Pith papers
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WiFo-CF: Wireless Foundation Model for CSI Feedback
WiFo-CF is a pretrained wireless foundation model that handles heterogeneous CSI feedback configurations and transfers to localization tasks.
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LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks
A frozen pre-trained vision model can extract wireless channel paths and features, beating conventional estimators in channel estimation and matching specialized networks in sensing with far fewer trainable parameters.
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BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization
A BERT-based transformer, BERT4beam, learns to output beamforming vectors from CSI and achieves near-SCA performance across multiple MU-MISO tasks and system scales.
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Joint Spatial Division and Multiplexing with Customized Orthogonal Group Channels in Multi-RIS-Assisted Systems
RISs placed at DFT directions of the base station enable a low-complexity JSDM pre-beamformer that approximately block-diagonalizes the multiuser channel and improves sum spectral efficiency in blocked coverage holes.
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Semantic-aware Digital Twin for AI-based CSI Acquisition
A vision paper proposing semantic-aware digital twins as both an auxiliary information source and a data/parameter generator for AI-based CSI acquisition in 6G.
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Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration
The paper proposes a systematic classification and two roadmaps for using foundation models (LLMs and wireless foundation models) to design Synesthesia of Machines systems for 6G, with preliminary case-study evidence ...
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Limited Feedback in RIS-Assisted Wireless Communications: Use Cases, Challenges, and Future Directions
A survey that organizes limited CSI feedback for RIS-assisted wireless communications around channel reconstruction and RIS configuration, highlighting structured sparsity and other channel features.
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