Fault Tolerance in Programmable Metasurfaces: The Beam Steering Case

Taghvaee, Hamidreza; Abadal, Sergi; Georgiou, Julius; Cabellos‐Aparicio, Albert; Alarcón, Eduard

doi:10.1109/iscas.2019.8702080

Cited by 15 publications

(16 citation statements)

References 39 publications

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“…Since the unit cells are not extremely small compared to the wavelength (

) and phase reflection is the only parameter that is different for each unit cell, the coupling effect between the unit cells is negligible. The accuracy of this approximation is in excellent agreement with full-wave simulations [ 33 , 36 , 42 ] and has been used in different analyses [ 54 , 55 , 56 ].…”

Section: Incremental Design Frameworksupporting

confidence: 54%

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Taghvaee

Jain

Timoneda

et al. 2021

Sensors

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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 is reconfigurable intelligent surfaces. 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 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 and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can 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 reconfigurable intelligent surfaces that will be deployed in the 6G network environment.

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“…Since the unit cells are not extremely small compared to the wavelength (

Section: Incremental Design Frameworksupporting

confidence: 54%

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Taghvaee

Jain

Timoneda

et al. 2021

Sensors

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“…1 [21]. This contribution extends our previous work [38] where the error model and the methodology were outlined, and a generic beam steering device was evaluated in a limited amount of cases. Here, we deepen the analysis by (i) introducing a realistic unit cell to improve the system model, (ii) exemplifying the effects of failures in the components of a tunable unit cell, (iii) evaluating the impact of faults in multiple performance metrics such as deviation from the target direction, and (iv) evaluating the impact of a much wider range of error type combinations.…”

Section: Introductionmentioning

confidence: 62%

Error Analysis of Programmable Metasurfaces for Beam Steering

Taghvaee

Cabellos‐Aparicio

Georgiou

et al. 2020

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Recent years have seen the emergence of programmable metasurfaces, where the user can modify the 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.

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“…Coupled to power consumption, cost, or application-specific models, our methodology will provide SDM/RIS designers and network architects with a clear picture of the practicable design space, illustrating the main tradeoffs and pointing to potentially optimal regions. Although programmable MSs have been the subject of sensitivity analyses [51]- [53], the impact of scaling fundamental design parameters has not been studied yet. Björnson et al studied the scaling of power in RIS environments, but considers conventional arrays rather than programmable MSs [54].…”

Section: Introductionmentioning

confidence: 99%

Scalability Analysis of Programmable Metasurfaces for Beam Steering

et al. 2020

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Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces 5λ×5λ or larger with unit cells of λ/3 and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than θ > 60 o and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal metasurface dimensioning.

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Fault Tolerance in Programmable Metasurfaces: The Beam Steering Case

Cited by 15 publications

References 39 publications

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Error Analysis of Programmable Metasurfaces for Beam Steering

Scalability Analysis of Programmable Metasurfaces for Beam Steering

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