Design and Development of Software Defined Metamaterials for Nanonetworks

Liaskos, Christos; Tsioliaridou, Ageliki; Pitsillides, Andreas; Akyildiz, Ian F.; Kantartzis, Nikolaos V.; Lalas, Antonios; Dimitropoulos, Xenofontas; Ioannidis, Sotiris; Kafesaki, Maria; Soukoulis, Costas M.

doi:10.1109/mcas.2015.2484098

Cited by 88 publications

(79 citation statements)

References 49 publications

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“…Programmable metasurfaces have opened the door to disruptive paradigms such as Software-Defined Metamaterials (SDMs) and Reconfigurable Intelligent Surfaces (RISs), leading to the interconnection of metasurfaces, the use of machine learning and, eventually, the implementation of softwaredriven distributed intelligence on EM control [19], [23]- [25]. This has potential to exert a disruptive impact in a plethora of application domains, including but not limited to holographic displays [7], stealth technology [26] or wireless communications [21], [28].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…1(c)]. To be realistic we have limited the achievable series capacitances in the range [1,5] pF. Even under this restriction, we access a large reflection phase span of 300 degrees while the reflection amplitude remains very close to unity (inset).…”

Section: Tunable Anomalous Reflection With Continuously Controllementioning

confidence: 99%

Tunable Perfect Anomalous Reflection in Metasurfaces with Capacitive Lumped Elements

Tsilipakos

Pitilakis

et al. 2018

2018 12th International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials)

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We demonstrate tunable perfect anomalous reflection with metasurfaces incorporating lumped elements. The tunable capacitance of each element provides continuous control over the local surface reactance, allowing for controlling the evanescent field distribution and efficiently tilting the reflected wavefront away from the specular direction. The performance of the metasurface is evaluated for both TE and TM polarization and for reflection to the first and second diffraction order.

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“…Rich multi-path conditions are generated as a result of these interactions which, if not well-controlled, could destructively influence the users' communication. In order to exert deterministic, adaptive control over the wireless propagation phenomenon, a novel solution based on artificial planar materials has been conceived, which utilizes softwaredefined metasurfaces (SDMs) [3]. SDMs sense and apply transformative control over EM waves impinging upon them, re-engineering their direction, polarization and phase, performing arbitrary wavefront shaping which indicatively includes focus, collimation and absorption ( Fig.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Neural Network for Configuring Programmable Wireless Environments

Liaskos

Tsioliaridou

Nie

et al. 2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Software-defined metasurfaces (SDMs) comprise a dense topology of basic elements called meta-atoms, exerting the highest degree of control over surface currents among intelligent panel technologies. As such, they can transform impinging electromagnetic (EM) waves in complex ways, modifying their direction, power, frequency spectrum, polarity and phase. A welldefined software interface allows for applying such functionalities to waves and inter-networking SDMs, while abstracting the underlying physics. A network of SDMs deployed over objects within an area, such as a floorplan walls, creates programmable wireless environments (PWEs) with fully customizable propagation of waves within them. This work studies the use of machine learning for configuring such environments to the benefit of users within. The methodology consists of modeling wireless propagation as a custom, interpretable, back-propagating neural network, with SDM elements as nodes and their crossinteractions as links. Following a training period the network learns the propagation basics of SDMs and configures them to facilitate the communication of users within their vicinity.

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Design and Development of Software Defined Metamaterials for Nanonetworks

Cited by 88 publications

References 49 publications

Error Analysis of Programmable Metasurfaces for Beam Steering

Error Analysis of Programmable Metasurfaces for Beam Steering

Tunable Perfect Anomalous Reflection in Metasurfaces with Capacitive Lumped Elements

An Interpretable Neural Network for Configuring Programmable Wireless Environments

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