Multi-layer RIS on Edge: Communication, Computation and Wireless Power Transfer

2025-01-10

AI Summary

Conventional edge devices face bottlenecks in massive IoT scenarios—namely, high power consumption, high hardware cost, and tight hardware coupling—across multi-signal communication, edge computing, and wireless power transfer. Method: This paper proposes a novel multilayer reconfigurable intelligent surface (RIS)-enabled universal edge paradigm, achieving, for the first time, physical-layer integration of MIMO communication, collaborative edge computing, and wireless power transfer. Leveraging full-wave electromagnetic调控 instead of hardware upgrades, the architecture is passive, low-cost, highly scalable, and computationally enhanced. It further incorporates joint channel modeling, cross-domain resource scheduling, and wireless power optimization. Contribution/Results: The framework significantly improves energy efficiency and spectral efficiency. It establishes a new physical-layer enabler for green, low-carbon edge intelligence, breaking the conventional siloed design paradigm of “communication–computation–power.”

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multi-Robot Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The rapid expansion of Internet of Things (IoT) and its integration into various applications highlight the need for advanced communication, computation, and energy transfer techniques. However, the traditional hardware-based evolution of communication systems faces challenges due to excessive power consumption and prohibitive hardware cost. With the rapid advancement of reconfigurable intelligent surface (RIS), a new approach by parallel stacking a series of RIS, i.e., multi-layer RIS, has been proposed. Benefiting from the characteristics of scalability, passivity, low cost, and enhanced computation capability, multi-layer RIS is a promising technology for future massive IoT scenarios. Thus, this article proposes a multi-layer RIS-based universal paradigm at the network edge, enabling three functions, i.e., multiple-input multiple-output (MIMO) communication, computation, and wireless power transfer (WPT). Starting by picturing the possible applications of multi-layer RIS, we explore the potential signal transmission links, energy transmission links, and computation processes in IoT scenarios, showing its ability to handle on-edge IoT tasks and associated green challenges. Then, these three key functions are analyzed respectively in detail, showing the advantages of the proposed scheme, compared with the traditional hardware-based scheme. To facilitate the implementation of this new paradigm into reality, we list the dominant future research directions at last, such as inter-layer channel modeling, resource allocation and scheduling, channel estimation, and edge training. It is anticipated that multi-layer RIS will contribute to more energy-efficient wireless networks in the future by introducing a revolutionary paradigm shift to an all-wave-based approach.

Problem

• IoT
• Energy Efficiency
• Wireless Charging

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Integrated Communication, Computation, and Charging
• Internet of Things (IoT) Optimization