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

TL;DR

This paper introduces a multi-layer RIS paradigm at the network edge that enhances communication, computation, and wireless power transfer for IoT, offering a scalable, low-cost, energy-efficient alternative to traditional hardware-based systems.

Contribution

It proposes a novel multi-layer RIS framework enabling integrated MIMO communication, computation, and wireless power transfer at the network edge, addressing energy and hardware challenges in IoT.

Findings

Multi-layer RIS enhances signal and energy transmission efficiency.

The scheme offers advantages over traditional hardware-based systems.

Future research directions include channel modeling and resource allocation.

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.