Using any surface to realize a new paradigm for wireless communications

Liaskos, Christos; Tsioliaridou, Ageliki; Pitsillides, Andreas; Ioannidis, Sotiris; Akyildiz, Ian F.

doi:10.1145/3192336

Cited by 123 publications

(135 citation statements)

References 10 publications

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“…Despite being applicable to any EM functionality, here we use the methodology to study a beam steering metasurface at 26 GHz as a particular yet very relevant use case in metasurfaceenabled 5G communications as shown in Fig. 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.…”

Section: Introductionmentioning

confidence: 93%

“…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]. In the latter case, the metasurfaces allow modifying the wireless channel by controlling the direction and phase of reflection.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 illustrates a plausible scenario where service would normally be disrupted due to blockage, but that metasurfaces are capable of redress by directing the reflections to the user. This is a true paradigm shift for wireless communications, as the recent explosion of works can attest [21], [25], [28]- [32], because the wireless channel has traditionally been an inescapable limiting factor. Alas, the transition from static to intelligent programmable metasurfaces has come at the cost of added design, fabrication, and embedding complexity.…”

Section: Introductionmentioning

confidence: 99%

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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: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…These exquisite capabilities have recently enabled the PWE concept [1]. PWEs are created by applying HSF coating over large planar objects, such as ceilings and walls in floorplans.…”

Section: Introductionmentioning

confidence: 99%

Mobility-Aware Beam Steering in Metasurface-Based Programmable Wireless Environments

Liaskos

Nie

Tsioliaridou

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Programmable wireless environments (PWEs) utilize electromagnetic metasurfaces to transform wireless propagation into a software-controlled resource. In this work we study the effects of user device mobility on the efficiency of PWEs. An analytical model is proposed, which describes the potential misalignment between user-emitted waves and the active PWE configuration, and can constitute the basis for studying queuing problems in PWEs. Subsequently, a novel, beam steering approach is proposed which can effectively mitigate the misalignment effects. Ray-tracing-based simulations evaluate the proposed scheme.

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“…1. The meta-atoms can have different shapes, including the shown split-ring structure and more complicated ones [9]. The total EM response of the metasurface is then derived as the total emitted field by all surface currents, and can take completely engineered forms, achieving custom phase shift, polarization tuning, and so on.…”

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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Using any surface to realize a new paradigm for wireless communications

Abstract: Programmable wireless environments use unique customizable software processes rather than traditional rigid channel models.

Cited by 123 publications

References 10 publications

Error Analysis of Programmable Metasurfaces for Beam Steering

Error Analysis of Programmable Metasurfaces for Beam Steering

Mobility-Aware Beam Steering in Metasurface-Based Programmable Wireless Environments

An Interpretable Neural Network for Configuring Programmable Wireless Environments

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