We present a dynamic metamaterial aperture for use in computational imaging schemes at microwave frequencies. The aperture consists of an array of complementary, resonant metamaterial elements patterned into the upper conductor of a microstrip line. Each metamaterial element contains two diodes connected to an external control circuit such that the resonance of the metamaterial element can be damped by application of a bias voltage. Through applying different voltages to the control circuit, select subsets of the elements can be switched on to create unique radiation patterns that illuminate the scene. Spatial information of an imaging domain can thus be encoded onto this set of radiation patterns, or measurements, which can be processed to reconstruct the targets in the scene using compressive sensing algorithms. We discuss the design and operation of a metamaterial imaging system and demonstrate reconstructed images with a 10:1 compression ratio. Dynamic metamaterial apertures can potentially be of benefit in microwave or millimeter wave systems such as those used in security screening and through-wall imaging. In addition, feature-specific or adaptive imaging can be facilitated through the use of the dynamic aperture.

Dynamic metamaterial aperture for microwave imaging

This article covers recent advances in the fusion of metasurface antenna design and computational imaging (CI) concepts for the realization of imaging systems that are planar, fast, and low cost. We start by explaining the operation of metamaterial antennas which can generate diverse radiation patterns. Their advantages and distinctions from previous antennas are elucidated. We then provide an intuitive overview of the CI framework and argue that metamaterial antennas are a near ideal platform for implementing such schemes at microwave frequencies. We describe two metamaterial antenna implementations: frequency diverse and electronically reconfigurable. The tradeoffs governing the design and operation of each architecture are examined. We conclude by examining the outlook of metamaterial antennas for microwave imaging and propose various future directions.

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Review of Metasurface Antennas for Computational Microwave Imaging

We investigate the imaging capabilities of a one-dimensional, dynamic, metamaterial aperture that operates at the lower part of K-band microwave frequencies (17.5–21.1 GHz). The dynamic aperture consists of a microstrip transmission line with an array of radiating, complementary, subwavelength metamaterial irises patterned into the upper conductor. Diodes integrated into the metamaterial resonators provide voltage-controlled switching of the resonant metamaterial elements between radiating and nonradiating states. Applying a series of on/off patterns to the metamaterial resonators produces a series of distinct radiation patterns that sequentially illuminate a scene. The backscattered signal contains encoded scene information over a set of measurements that can be postprocessed to reconstruct an image. We present a series of design considerations for the dynamic aperture, as well as a series of experimental studies performed using a dynamic aperture prototype. High-fidelity, real-time, diffraction-limited imaging using the prototype is demonstrated. The dynamic aperture suggests a path to fast and reliable imaging with low-cost and versatile hardware, for a variety of applications including security screening, biomedical diagnostics, and through-wall imaging.

Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies

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 paper, 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. First, we 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.

Dynamic Metasurface Antennas for Uplink Massive MIMO Systems

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.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.201901913

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

We present the design of a tunable metamaterial element that can serve as the building block for a dynamically reconfigurable aperture. The element-a complimentary electric-LC (cELC) resonator-is patterned into the upper conductor of a microstrip transmission line, providing both a means of exciting the radiating metamaterial element as well as independent access for biasing circuitry. PIN diodes are connected across the capacitive gaps of the cELC, and a dc bias current is used to switch the junction between conducting and insulating states. The leakage of RF signal through the bias line is mitigated by integration of a radial decoupling stub. The proposed design and operation of the element are demonstrated through full-wave electromagnetic simulations. We discuss the potential application of the cELC element as a building block for metamaterial apertures capable of dynamic beamforming, imaging, or security screening applications.

Wangren Xu

Papers

Waveguide-Fed Tunable Metamaterial Element for Dynamic Apertures