Waveguide-Fed Tunable Metamaterial Element for Dynamic Apertures

Sleasman, Timothy; Imani, Mohammadreza F.; Xu, Wangren; Hunt, John; Driscoll, Tom; Reynolds, Matthew S.; Smith, David R.

doi:10.1109/lawp.2015.2462818

Cited by 118 publications

(72 citation statements)

References 19 publications

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“…[4] We focus on optimally configuring dynamic metasurface hardware, a promising alternative to more traditional antenna arrays for beam-forming and wavefront shaping. [30] Compared to a traditional antenna array that uses amplifiers and phase shifters, the inherent analog multiplexing makes dynamic metasurface hardware much simpler, less costly and easier to integrate in many applications (see Section IV in the Supporting Information). [28,29] Reconfigurability is achieved by individually shifting each metamaterial element's resonance frequency, for instance, with a PIN diode.…”

Section: Lisp For Object Recognition With Dynamic Metasurface Aperturesmentioning

confidence: 99%

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

et al. 2019

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The rapid proliferation of intelligent systems (e.g., fully autonomous vehicles) in today's society relies on sensors with low latency and computational effort. Yet current sensing systems ignore most available a priori knowledge, notably in the design of the hardware level, such that they fail to extract as much task‐relevant information per measurement as possible. Here, a “learned integrated sensing pipeline” (LISP), including in an end‐to‐end fashion both physical and processing layers, is shown to enable joint learning of optimal measurement strategies and a matching processing algorithm, making use of a priori knowledge on task, scene, and measurement constraints. Numerical results demonstrate accuracy improvements around 15% for object recognition tasks with limited numbers of measurements, using dynamic metasurface apertures capable of transceiving programmable microwave patterns. Moreover, it is concluded that the optimal learned microwave patterns are nonintuitive, underlining the importance of the LISP paradigm in current sensorization trends.

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Section: Lisp For Object Recognition With Dynamic Metasurface Aperturesmentioning

confidence: 99%

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

et al. 2019

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“…1. a) An example E-plane radiation pattern for the antenna used in this work and b) a schematic of the aperture, its RF feed, and the control circuitry. More details about the cell and the aperture biasing network can be found in [17,18].…”

Section: Introductionmentioning

confidence: 99%

Single-frequency microwave imaging with dynamic metasurface apertures

et al. 2017

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Conventional microwave imaging schemes, enabled by the ubiquity of coherent sources and detectors, have traditionally relied on frequency bandwidth to retrieve range information, while using mechanical or electronic beamsteering to obtain cross-range information. This approach has resulted in complex and expensive hardware when extended to large-scale systems with ultrawide bandwidth. Relying on bandwidth can create difficulties in calibration, alignment, and imaging of dispersive objects. We present an alternative approach using electrically-large, dynamically reconfigurable, metasurface antennas that generate spatially-distinct radiation patterns as a function of tuning state. The metasurface antenna consists of a waveguide feeding an array of metamaterial radiators, each with properties that can be modified by applying a voltage to diodes integrated into the element. By deploying two of these apertures, one as the transmitter and one as the receiver, we realize sufficient spatial diversity to alleviate the dependence on frequency bandwidth and obtain both range and cross-range information using measurements at a single frequency. We experimentally demonstrate this proposal by using two one-dimensional dynamic metasurface apertures and reconstructing various two-dimensional scenes (range and cross-range). Furthermore, we modify a conventional reconstruction method-the range migration algorithm-to be compatible with such configurations, resulting in an imaging system that is efficient in both software and hardware. The imaging scheme presented in this paper has broad application to radio frequency imaging, including security screening, through-wall imaging, biomedical diagnostics, and synthetic aperture radar.

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“…In fact, any two-dimensional DMA in which each element is connected to a single output port can be represented via (2) by modifying the structure of the weights matrix Q to represent the resulting elements interconnections. DMAs integrate a tuning mechanism into each independent resonator of a metasurface antenna [29]. The dynamic tuning adds the flexibility to adjust the properties of the metamaterial elements, namely, to control the values of the coefficients {q p,l } in (1).…”

Section: A Dynamic Metasurface Antennasmentioning

confidence: 99%

“…Recently, dynamic metasurface antennas (DMAs) have been proposed as a method for electrically tuning the physical characteristics of metamaterial antennas [20], [28], [29]. DMAs inherently implement signal processing techniques such as beamforming, analog combining, compression, and antenna selection, without additional hardware.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Metasurface Antennas for Uplink Massive MIMO Systems

Shlezinger

Dicker²,

Eldar

et al. 2019

IEEE Trans. Commun.

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Massive multiple-input multiple-output (MIMO) communications are the focus of considerable interest in recent years. While the theoretical gains of massive MIMO have been established, implementing MIMO systems with large-scale antenna arrays in practice is challenging. Among the practical challenges associated with massive MIMO systems are increased cost, power consumption, and physical size. In this work we study the implementation of massive MIMO antenna arrays using dynamic metasurface antennas (DMAs), an emerging technology which inherently handles the aforementioned challenges. Specifically, DMAs realize large-scale planar antenna arrays, and can adaptively incorporate signal processing methods such as compression and analog combining in the physical antenna structure, thus reducing the cost and power consumption. We first propose a mathematical model for massive MIMO systems with DMAs and discuss their constraints compared to ideal antenna arrays. Then, we characterize the fundamental limits of uplink communications with the resulting systems, and propose two algorithms for designing practical DMAs for approaching these limits. Our numerical results indicate that the proposed approaches result in practical massive MIMO systems whose performance is comparable to that achievable with ideal antenna arrays.

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Waveguide-Fed Tunable Metamaterial Element for Dynamic Apertures

Cited by 118 publications

References 19 publications

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

Single-frequency microwave imaging with dynamic metasurface apertures

Dynamic Metasurface Antennas for Uplink Massive MIMO Systems

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