Fault Tolerance in Programmable Metasurfaces: The Beam Steering Case

Metasurfaces, the two-dimensional counterpart of metamaterials, have caught great attention thanks to their powerful control over electromagnetic waves. Recent times have seen the emergence of a variety of metasurfaces exhibiting not only countless functionalities, but also a reconfigurable or even programmable response. Reconfigurability, however, entails the integration of tuning and control circuits within the metasurface structure and, as this new paradigm moves forward, new reliability challenges may arise. This paper examines, for the first time, the reliability problem in programmable metamaterials by proposing an error model and a general methodology for error analysis. To derive the error model, the causes and potential impact of faults are identified and discussed qualitatively. The methodology is presented and instantiated for beam steering, which constitutes a relevant example for programmable metasurfaces. Results show that performance degradation depends on the type of error and its spatial distribution and that, in beam steering, error rates over 10% can still be considered acceptable

Error analysis of programmable metasurfaces for beam steering

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Recent years have seen the emergence of programmable metasurfaces, where the user can modify the electromagnetic (EM) response of the device via software. Adding reconfigurability to the already powerful EM capabilities of metasurfaces opens the door to novel cyber-physical systems with exciting applications in domains such as holography, cloaking, or wireless communications. This paradigm shift, however, comes with a non-trivial increase of the complexity of the metasurfaces that will pose new reliability challenges stemming from the need to integrate tuning, control, and communication resources to implement the programmability. While metasurfaces will become prone to failures, little is known about their tolerance to errors. To bridge this gap, this paper examines the reliability problem in programmable metamaterials by proposing an error model and a general methodology for error analysis. To derive the error model, the causes and potential impact of faults are identified and discussed qualitatively. The methodology is presented and exemplified for beam steering, which constitutes a relevant case for programmable metasurfaces. Results show that performance degradation depends on the type of error and its spatial distribution and that, in beam steering, error rates over 20% can still be considered acceptable.This work has been supported by the European Commission under grant H2020-FETOPEN-736876 (VISORSURF) and by ICREA under the ICREA Academia programme. The person and base station icons in Figure 1 were created by Jens Tärningand Clea Doltz from the Noun Project.Peer ReviewedPostprint (author's final draft

Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning

The fifth generating (5G) of wireless networks will be more adaptive and heterogeneous. Reconfigurable intelligent surface technology enables the 5G to work on multistrand waveforms. However, in such a dynamic network, the identification of specific modulation types is of paramount importance. We present a RIS-assisted digital classification method based on artificial intelligence. We train a convolutional neural network to classify digital modulations. The proposed method operates and learns features directly on the received signal without feature extraction. The features learned by the convolutional neural network are presented and analyzed. Furthermore, the robust features of the received signals at a specific SNR range are studied. The accuracy of the proposed classification method is found to be remarkable, particularly for low levels of SNR

Perfect-Lens Theory Enables Metasurface Reflectors for Subwavelength Focusing

Breaking the so-called diffraction limit on the resolution of optical devices and achieving subwavelength focusing requires tailoring the evanescent spectrum of wave fields. There are several possible approaches, all of which have limitations, such as the generation of strong additional scattering, limited focusing power, issues at the implementation step, and the need for a drain at the focal point. This paper presents a feasible strategy based on the concepts of the perfect lens and power flow-conformal metasurfaces. Desired fields for subwavelength focusing are integrated using double-negative media and then the surface profile of a focusing reflector is designed to be tangential to the desired power flow, so that the metasurface can be modeled as a local impedance boundary, and can be easily implemented using passive and lossless elements. Full-wave simulations demonstrate that an example reactive metasurface is able to break the diffraction limit and provide near-field focusing with subwavelength hotspot size. We expect that the outcome will find applications in antennas, beam-shaping devices, nonradiative wireless power transfer systems, microscopy, and lithography

Radiation pattern prediction for Metasurfaces: A Neural Network based approach

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks are Reconfigurable Intelligent Surfaces (RISs). They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface (MSF) is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full wave simulations, they suffer from inaccuracy under certain conditions and extremely high computational complexity, respectively. Hence, in this paper we propose a novel neural networks based approach that enables a fast and accurate characterization of the MSF response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method is able to learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full wave simulation (98.8%-99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance and maintenance of the thousands of RISs that will be deployed in the 6G network environment.Comment: Submitted to IEEE OJ-COM

On the Enabling of Multi-user Communications with Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surface (RIS) composed of programmable actuators is a promising technology, thanks to its capability in manipulating Electromagnetic (EM) wavefronts. In particular, RISs have the potential to provide significant performance improvements for wireless networks. However, to do so, a proper configuration of the reflection coefficients of the unit cells in the RIS is required. RISs are sophisticated platforms so the design and fabrication complexity might be uneconomical for single-user scenarios while a RIS that can service multi-users justifies the costs. For the first time, we propose an efficient reconfiguration technique providing the multi-beam radiation pattern. Thanks to the analytical model the reconfiguration profile is at hand compared to time-consuming optimization techniques. The outcome can pave the wave for commercial use of multi-user communication beyond 5G networks. We analyze the performance of our proposed RIS technology for indoor and outdoor scenarios, given the broadcast mode of operation. The aforesaid scenarios encompass some of the most challenging scenarios that wireless networks encounter. We show that our proposed technique provisions sufficient gains in the observed channel capacity when the users are close to the RIS in the indoor office environment scenario. Further, we report more than one order of magnitude increase in the system throughput given the outdoor environment. The results prove that RIS with the ability to communicate with multiple users can empower wireless networks with great capacity

Intelligent Beam Steering for Wireless Communication Using Programmable Metasurfaces

Reconfigurable Intelligent Surfaces (RIS) are well established as a promising solution to the blockage problem in millimeter-wave (mm-wave) and terahertz (THz) communications, envisioned to serve demanding networking applications, such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary enabling technology for RIS, complementing Software Defined Metasurfaces (SDM) with an embedded network of controllers to enhance intelligence and autonomous operation in wireless networks. In this work, we consider feedback-based autonomous reconfiguration of the HSF controller states to establish a reliable communication channel between a transmitter and a receiver via programmable reflection on the HSF when Line-of-sight (LoS) between them is absent. The problem is to regulate the angle of reflection on the metasurface such that the power at the receiver is maximized. Extremum Seeking Control (ESC) is employed with the control signals generated mapped into appropriate metasurface coding signals which are communicated to the controllers via the embedded controller network (CN). This information dissemination process incurs delays which can compromise the stability of the feedback system and are thus accounted for in the performance evaluation. Extensive simulation results demonstrate the effectiveness of the proposed method to maximize the power at the receiver within a reasonable time even when the latter is mobile. The spatiotemporal nature of the traffic for different sampling periods is also characterized

Graphene-based wireless agile interconnects for massive heterogeneous multi-chip processors

The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In such data-hungry multi-chip architectures, current Networks-in-Package (NiPs) may not be enough to cater to their heterogeneous and fast-changing communication demands. This position article makes the case for wireless in-package networking as the enabler of efficient and versatile wired-wireless interconnect fabrics for massive heterogeneous processors. To that end, the use of graphene-based antennas and transceivers with unique frequency-beam reconfigurability in the terahertz band is proposed. The feasibility of such a wireless vision and the main research challenges toward its realization are analyzed from the technological, communications, and computer architecture perspectives.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work has been also supported by the European Commission under H2020 grants WiPLASH (GA 863337), 2D-EPL (GA 952792), and Graphene Flagship (GA 881603); the FLAGERA framework under grant TUGRACO (HA 3022/9-1, LE 2440/3-1), the European Research Council under grants WINC (GA 101042080), COMPUSAPIEN (GA 725657), and PROJESTOR (GA 682675), the German Ministry of Education and Research under grant GIMMIK (03XP0210) and the and the German Research Foundation under grant HIPEDI (WA 4139/1-1).Peer ReviewedArticle signat per 21 autors/es: Sergi Abadal, Robert Guirado, Hamidreza Taghvaee, and Akshay Jain are with the Universitat Politècnica de Catalunya, Spain; Elana Pereira de Santana and Peter Haring Bolívar are with the University of Siegen, Germany; Mohamed Saeed, Renato Negra, Kun-Ta Wang, and Max C. Lemme are with RWTH Aachen University, Germany. Zhenxing Wang, Kun-Ta Wang, and Max C. Lemme are also with AMO GmbH, Germany; Joshua Klein, Marina Zapater, Alexandre Levisse, and David Atienza are with the Swiss Federal Institute of Technology, Switzerland. Marina Zapater is also with the University of Applied Sciences and Arts Western Switzerland; Davide Rossi and Francesco Conti are with the University of Bologna,Italy; Martino Dazzi, Geethan Karunaratne, Irem Boybat, and Abu Sebastian are with IBM Research Europe, SwitzerlandPostprint (author's final draft

Tunable graphene-based metasurfaces for multi-wideband 6G communications

The next generation of wireless communications within the framework of 6G will be operational at the low THz frequency band. Although THz systems will dramatically enhance several performance indicators such as the data rate, spectral efficiency, and latency, exploiting such technology is challenging. Electromagnetic waves confront severe propagation losses including atmospheric attenuation and diffraction. Thus, such communications are limited to line-of-sight scenarios. In 5G networks, Reconfigurable Intelligent Surfaces (RISs) are intro-duced to solve this issue by redirecting the incident wave toward the receiver and implement virtual-line-of-sight communications. In this paper, we aim to employ this paradigm for 6G networks and design a graphene-based RIS optimized to perform at multiple low atmospheric attenuation channels. We investigate the performance of this multi-wideband design through numerical and analytical analysis.Peer ReviewedPostprint (author's final draft

On the use of programmable metasurfaces in vehicular networks

Metasurface-based intelligent reflecting surfaces constitute a revolutionary technology which can serve the purpose of alleviating the blockage problem in mmwave communication systems. In this work, we consider the hypersurface paradigm complementing the software defined metasurface with an embedded controller network in order to facilitate the dissemination of reconfiguration directives to unit cell controllers. For the first time, we describe the methodology with which to characterize the workload within this embedded network in the case of the metasurface tracking multiple users and we use a vehicular communications setting to showcase the methodology. Beyond that, we demonstrate use cases of the workload analysis. We show how the workload characterization can guide the design of information dissemination schemes achieving significant reduction in the network traffic. Moreover, we show how the workload, as a measure of the consumed power, can be used in designing energy efficient communication protocols through a multi-objective optimization problem maximizing the achieved utilization while at the same time minimizing the workload incurred.Peer ReviewedPostprint (author's final draft