Limited Feedback in RIS-Assisted Wireless Communications: Use Cases, Challenges, and Future Directions

2025-06-28

AI Summary

In RIS-assisted wireless communication, limited channel state information (CSI) feedback arises due to the absence of baseband processing capability at RIS elements, exacerbated by location-dependent channel variations, extremely high-dimensional subchannel matrices, and inherent structural sparsity. Method: This paper proposes a lightweight CSI feedback framework integrating channel customization, structural sparsity modeling, and a deep autoencoder. Leveraging multimodal channel priors and an AI-driven joint compression–reconstruction mechanism, it achieves efficient approximation of RIS reflection coefficients. Contribution/Results: The proposed scheme significantly reduces feedback bit overhead and terminal computational complexity while maintaining system performance. It enhances scalability and practical deployability of RIS-aided networks, offering a novel paradigm for limited-feedback RIS systems that balances theoretical rigor with engineering feasibility.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Channel state information (CSI) is essential to unlock the potential of reconfigurable intelligent surfaces (RISs) in wireless communication systems. Since massive RIS elements are typically implemented without baseband signal processing capabilities, limited CSI feedback is necessary when designing the reflection/refraction coefficients of the RIS. In this article, the unique RIS-assisted channel features, such as the RIS position-dependent channel fluctuation, the ultra-high dimensional sub-channel matrix, and the structured sparsity, are distilled from recent advances in limited feedback and used as guidelines for designing feedback schemes. We begin by illustrating the use cases and the corresponding challenges associated with RIS feedback. We then discuss how to leverage techniques such as channel customization, structured-sparsity, autoencoders, and others to reduce feedback overhead and complexity when devising feedback schemes. Finally, we identify potential research directions by considering the unresolved challenges, the new RIS architecture, and the integration with multi-modal information and artificial intelligence.

Problem

• Address limited CSI feedback in RIS-assisted wireless systems
• Reduce feedback overhead using sparsity and autoencoder techniques
• Explore future RIS architectures with AI integration

Innovation

• Leverage RIS position-dependent channel features
• Use structured-sparsity to reduce feedback overhead
• Integrate autoencoders for feedback complexity reduction