Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and Networking (TCCN

Reconfigurable Intelligent Surface Enabled Joint Backscattering and Communication

Reconfigurable intelligent surface (RIS) as an essential topic in the sixth-generation (6G) communications aims to enhance communication performance or mitigate undesired transmission. However, the controllability of each reflecting element on RIS also enables it to act as a passive backscatter device (BD) and transmit its information to reader devices. In this paper, we propose a RIS-enabled joint backscattering and communication (JBAC) system, where the backscatter communication coexists with the primary communication and occupies no extra spectrum. Specifically, the RIS modifies its reflecting pattern to act as a passive BD and reflect its own information back to the base station (BS) in the backscatter communication, while helping the primary communication from the BS to the users simultaneously. We further present an iterative active beamforming and reflecting pattern design to maximize the user average transmission rate of the primary communication and the goodput of the backscatter communication by solving the formulated multi-objective optimization problem (MOOP). Numerical results fully uncover the impacts of the number of reflecting elements and the reflecting patterns on the system performance, and demonstrate the effectiveness of the proposed scheme. Important practical implementation remarks have also been discussed.Comment: 11 pages, 8 figures, published to IEEE TV

RIScatter: unifying backscatter communication and reconfigurable intelligent surface

Backscatter Communication (BackCom) nodes harvest energy from and modulate information over an external electromagnetic wave. Reconfigurable Intelligent Surface (RIS) adapts its phase shift response to enhance or attenuate channel strength in specific directions. In this paper, we show how those two seemingly different technologies (and their derivatives) can be unified to leverage their benefits simultaneously into a single architecture called RIScatter. RIScatter consists of multiple dispersed or co-located scatter nodes, whose reflection states can be adapted to partially engineer the wireless channel of the existing link and partially modulate their own information onto the scattered wave. This contrasts with BackCom (resp. RIS) where the reflection pattern is exclusively a function of the information symbol (resp. Channel State Information (CSI)). The key principle in RIScatter is to render the probability distribution of reflection states (i.e., backscatter channel input) as a joint function of the information source, CSI, and Quality of Service (QoS) of the coexisting active primary and passive backscatter links. This enables RIScatter to softly bridge, generalize, and outperform BackCom and RIS; boil down to either under specific input distribution; or evolve in a mixed form for heterogeneous traffic control and universal hardware design. For a single-user multi-node RIScatter network, we characterize the achievable primary-(total-)backscatter rate region by optimizing the input distribution at the nodes, the active beamforming at the Access Point (AP), and the backscatter detection regions at the user. Simulation results demonstrate RIScatter nodes can exploit the additional propagation paths to smoothly transition between backscatter modulation and passive beamforming

RIS-Assisted Integrated Sensing and Backscatter Communications for Future IoT Networks

Reconfigurable intelligent surface (RIS), by intelligently manipulating the incident waveform, offers a spectral and energy efficient capability for improving sensing and communication performance. In this article, we introduce a novel concept of RIS-assisted integrated sensing and backscatter communication (ISABC) system, by introducing RIS as either helper or transceiver to resolve the energy constraint of devices in internet of things (IoT) network and enable non line-of-sight (NLoS) sensing. We first introduce the RIS-assisted ISABC framework, including the system architecture and realization of RIS. Three potential applications are then discussed, with the analysis on their requirements. The research on several critical techniques for the RIS-assisted ISABC system is then discussed. Finally, we provide our vision of the challenges and future research directions to facilitate the development of the RIS-assisted ISABC systems

Computation Offloading for Edge Computing in RIS-Assisted Symbiotic Radio Systems

In the paper, we investigate the coordination process of sensing and computation offloading in a reconfigurable intelligent surface (RIS)-aided base station (BS)-centric symbiotic radio (SR) systems. Specifically, the Internet-of-Things (IoT) devices first sense data from environment and then tackle the data locally or offload the data to BS for remote computing, while RISs are leveraged to enhance the quality of blocked channels and also act as IoT devices to transmit its sensed data. To explore the mechanism of cooperative sensing and computation offloading in this system, we aim at maximizing the total completed sensed bits of all users and RISs by jointly optimizing the time allocation parameter, the passive beamforming at each RIS, the transmit beamforming at BS, and the energy partition parameters for all users subject to the size of sensed data, energy supply and given time cycle. The formulated nonconvex problem is tightly coupled by the time allocation parameter and involves the mathematical expectations, which cannot be solved straightly. We use Monte Carlo and fractional programming methods to transform the nonconvex objective function and then propose an alternating optimization-based algorithm to find an approximate solution with guaranteed convergence. Numerical results show that the RIS-aided SR system outperforms other benchmarks in sensing. Furthermore, with the aid of RIS, the channel and system performance can be significantly improved.Comment: 13 pages, 7 figure