Multi-functional RIS architectures and algorithms for next-generation 6G networks

Existing reconfigurable intelligent surface literature focuses predominantly on single-functional architectures limited to signal reflection alone. Multi-Functional Reconfigurable Intelligent Surfaces for 6G: RIS 2.0 Architectures, Algorithms, and Applications advances the field by presenting MF-RIS, a multi-functional architecture capable of simultaneous signal reflection, refraction, amplification, energy harvesting, and target sensing within unified hardware designs for 6G wireless networks.

The book covers coefficient and channel models for MF-RIS configurations, beamforming techniques under non-ideal conditions, and simulations validating performance. Specific categories examined include omni-amplifying, self-sustainable, and target-sensing architectures. Chapters connect MF-RIS with federated learning, semantic communication, and AI-driven metasurface design, while addressing standardization pathways and health and safety compliance.

Readers will also find:

• Hardware architecture details for multi-functional RIS elements supporting multiple simultaneous operations beyond conventional single-functional reflective surfaces
• Beamforming algorithm design addressing practical non-ideal deployment conditions encountered in real-world wireless communication system implementations
• Integration frameworks connecting MF-RIS with federated learning and semantic communication paradigms for intelligent network optimization
• Economic feasibility analysis and standardization considerations guiding practical deployment decisions for MF-RIS in commercial 6G networks
• Practical simulation examples demonstrating MF-RIS performance gains across diverse wireless communication scenarios and network configurations

Designed for telecommunications researchers, engineers developing 6G network infrastructure, and graduate students studying advanced wireless communication systems, this book provides the architectural foundations, algorithmic frameworks, and application-level analysis needed to design and deploy multi-functional reconfigurable intelligent surfaces in next-generation networks.

Wanli Ni, PhD, is an Assistant Professor with the School of Information and Communication Engineering, BUPT, China. He received Outstanding Doctoral Dissertation Awards from CESE and BUPT in 2023, the Best Paper Award from IEEE SAGC 2020, and was recognized as an IEEE Exemplary Reviewer four times.

Ailing Zheng is currently pursuing Ph.D. degree with the School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China. Her research focuses on reconfigurable intelligent surfaces and wireless resource management. She received the National Scholarship twice during her studies at BUPT.

Wen Wang, PhD, is a Postdoctoral Research Fellow with the Pervasive Communications Center, Purple Mountain Laboratories, and the National Mobile Communications Research Laboratory, Southeast University, Nanjing, China. She received the B.Eng. and Ph.D. degrees from BUPT in 2020 and 2024, respectively. Her research focuses on wireless resource management and machine learning applications.