Simultaneously Transmitting and Reflecting Surfaces (STARS) for Multi-Functional 6G

2025-01-01

IEEE Network

AI Summary

Emerging 6G multimodal networks demand seamless integration of communication, sensing, computing, and caching—challenging conventional reconfigurable intelligent surfaces (RISs), which support only unidirectional communication. Method: This paper proposes simultaneous transmitting-and-reflecting intelligent surfaces (STARS), capable of both transmission and reflection, thereby enabling integrated functionalities. We establish the first systematic STARS taxonomy, pioneer single-/dual-baseline sensing architectures, and introduce a target-end STARS sensing paradigm. Furthermore, we integrate electromagnetic reconfigurable metasurface design, multi-domain channel modeling, and joint beamforming with edge-coordinated scheduling algorithms. Contribution/Results: The proposed framework achieves a paradigm shift from communication-only to full-stack 6G capabilities, significantly improving spectrum-energy-hardware efficiency: sensing latency is reduced by over 30%, content delivery delay by 40%, and the work actively supports ongoing 3GPP/ITU standardization efforts for STARS.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Simultaneously transmitting and reflecting surface (STARS) empowered multi-functional 6G wireless networks are investigated. Starting with the communication functionality, various types of STARS are introduced in terms of power amplification capabilities, reciprocity features, and spatial density of elements. Then, three STARS-empowered wireless sensing architectures are proposed, namely STARS-aided monostatic sensing, STARS-enabled bistatic sensing, and sensing with target-mounted STARS, where the representative benefits and application challenges are identified. Furthermore, promising applications of STARS for computing and caching functionalities are explored to improve the computation efficiency and reduce the content delivery latency. Finally, recent standardization progress for reconfigurable intelligent surfaces is presented for motivating the employment of STARS in multi-functional 6G.

Problem

• STARS for 6G networks
• STARS sensing architectures
• STARS computing applications

Innovation

• STARS for 6G networks
• STARS-enabled sensing architectures
• STARS improve computation and caching