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Abstract Owing to the ability of reconfigurable intelligent surfaces (RISs) to control the propagation environment in their vicinity, they have emerged as an appealing solution to enhance the performance of next-generation wireless networks. While the effectiveness of RISs with complete channel state information (CSI) is well-documented, their performance with limited user information remains less explored. This is particularly pronounced when an RIS interferes with nodes in a potentially non-cooperating network, making CSI acquisition challenging. This paper focuses on such scenarios, examining the interference suppression capabilities of an RIS for coexisting secondary and primary users (SUs and PUs) using angular information, achievable even with non-cooperating PUs. We address the coexistence challenge by employing semidefinite relaxation (SDR) and inner majorization minimization (iMM) for RISs with continuous phase control, and propose an enhanced SDR tailored for discrete RISs. Numerical results reveal both SDR and iMM require at least five bits of discrete phase control for moderate interference suppression. They also indicate that interference suppression weakens if the PUs are inside certain angular regions, a limitation that varies with the algorithm but can be mitigated by increasing RIS elements. Ultimately, the coexistence performance of the RIS with a non-cooperative network is validated through full-wave simulations.

Large scale sensing systems increasingly operate in short-range geometries, providing three-dimensional imaging capabilities that leverage the large number of degrees of freedom of distributed arrays. The two most powerful modes of operation include active, multiple-input multiple-output arrays, in which an image is processed from signals measured using different pairs of transmitting and receiving antennas; and passive, synthetic aperture interferometric radiometer arrays, which can image using ambient, non-cooperative radiation by processing signals measured at different receiving antenna pairs. In this paper, we explore the analogy between these two imaging modes, and outline a generalized framework for analyzing these systems in the array near field. Focusing on the similarities between the formalisms describing the operation of generic active and passive systems, it is proposed, to our knowledge for the first time in the scientific literature, to adapt a Fourier-based imaging technique optimized for short-range active MIMO systems to passive technologies, interrogating the emissivity of a spatially distributed target. The systematic method presented in this paper provides a framework for characterizing and optimizing short range coherent or incoherent systems used in threat detection and non-destructive testing applications.

Reconfigurable reflective surfaces can form prescribed radiation patterns given a known incident signal, a capability with widespread use for beamforming in radar and imaging systems or for altering the propagation environment in wireless communication networks. For reflective surfaces to operate intelligently, they need to acquire the relevant information about the incident and reflection directions autonomously. Toward this goal, we design a metasurface that can generate reconfigurable radiation patterns as well as perform compressive sensing of the incident signals, allowing it to adapt its response without requiring feedback loops or dedicated links to transmitters or receivers—paving the way for autonomous and smart operation. To realize such an operation, we propose a metasurface consisting of two types of elements, conventional and hybrid. While both elements are designed to reflect the signal, the hybrid ones allow coupling of a small portion of the incident signal into a sensing layer. To ensure low loss and complexity, the number of hybrid elements and the associated sensing circuitry need to be kept small, which can degrade sensing performance. To circumvent this issue, we develop a compressive sensing scheme that leverages the inherent multiplexing of information within the metasurface’s substrate to retrieve relevant information using a few sensing RF chains, thus guaranteeing low cost and complexity. As an illustrative example, we numerically demonstrate the possibility to detect angles of arrivals of single and multiple incident beams using full-wave simulations of the proposed structure. We also show that the proposed metasurface can form desired radiation patterns and the introduction of the sensing capabilities has a minimal impact on its main functionality. The proposed smart reconfigurable surface with sensing capabilities may operate autonomously and can benefit wireless communication, wireless power transfer, and imaging systems.

Abstract Reconfigurable reflective surfaces can alter the propagation environment to improve wireless communication and power transfer. Paramount to this operation—which has attracted much attention recently—is the assumption that the reflective surface has prior knowledge of the propagation environment, for example, the direction/location of the transmitter and the intended receiver(s). To address this need, we propose a reconfigurable reflective metasurface with integrated sensing capabilities. By modifying the tunable meta-atoms constituting the metasurface, we couple small portions of the incident wave to an array of sensing waveguides. As an illustrative example, we demonstrate the ability to use the sampled incident wave to detect its angle of arrival. In addition, we propose and numerically demonstrate the possibility to reduce the required sensors, i.e., the number of radio frequency (RF) chains needed to acquire the sensed signals, by leveraging the inherent metasurface’s tunable multiplexing capability. A reconfigurable reflective metasurface with integrated sensing capabilities can benefit wireless communications, wireless power transfer, RF sensing, and smart sensors.

Abstract This paper introduces the concept of smart radio environments, currently intensely studied for wireless communication in metasurface‐programmable meter‐scaled environments (e.g., inside rooms), on the chip scale. Wireless networks‐on‐chips (WNoCs) are a candidate technology to improve inter‐core communication on chips but current proposals are plagued by a dilemma: either the received signal is weak, or it is significantly reverberated such that the on–off‐keying modulation speed must be throttled. Here, this vexing problem is overcome by endowing the wireless on‐chip environment with in situ programmability which enables the shaping of the channel impulse response (CIR); thereby, a pulse‐like CIR shape can be imposed despite strong multipath propagation and without entailing a reduced received signal strength. First, a programmable metasurface suitable for integration in the on‐chip environment (“on‐chip reconfigurable intelligent surface”) is designed and characterized. Second, its configuration is optimized to equalize selected wireless on‐chip channels “over the air.” Third, by conducting a rigorous communication analysis, the feasibility of significantly higher modulation speeds with shaped CIRs is evidenced. The results introduce a programmability paradigm to WNoCs which boosts their competitiveness as complementary on‐chip interconnect solution.

Abstract Mobile devices, climate science, and autonomous vehicles all require advanced microwave antennas for imaging, radar, and wireless communications. We propose a waveguide-fed metasurface antenna architecture that enables electronic beamsteering from a lightweight circuit board with varactor-tuned elements. Our approach uses a unique feed structure and layout that enables spatial sampling at the Nyquist limit of half a wavelength. We detail the design of this Nyquist metasurface antenna and experimentally demonstrate electronic beamsteering in two directions. Nyquist metasurface antennas can realize high performance without costly and power hungry phase shifters, making them a compelling technology for future antenna hardware.

Sensing motion and distinguishing its source as human or nonhuman, with high precision, has tremendous applications in a variety of areas from health monitoring to energy efficiency. One strategy to achieve this goal is to detect the small motion of breathing, which is a consistent indicator of human presence. Among the many smart sensing schemes proposed, microwave and RF sensors have shown great promise due to their simplicity, privacy, and effective range. In this paper, we propose the use of a dynamic metasurface antenna (DMAs) as an alternative hardware platform for sensing motion inside a residential setting using microwave signals. The proposed device is a single-port planar cavity that excites an array of electronically-tunable metamaterial elements. The DMA can generate spatially diverse patterns at a single frequency, avoiding complexities related to wideband operation or high hardware costs of antenna arrays. We demonstrate that it is capable of detecting minute movements, such as breathing emulated by a mannequin, to distinguish human presence. This motion can be detected whether the target is in the the sensor's direct line of sight or out of the direct line of sight. Furthermore, we show that the DMA sensing platform requires a single noise-floor calibration and can operate in different room geometries or configurations (e.g. when furniture is displaced). The proposed DMA-sensor with its single frequency operation and simple hardware is an appealing alternative hardware for intruder detection, human presence detection/activity recognition in smart homes, or seamless health monitoring.

We demonstrate that dynamic metasurface apertures (DMAs) are capable of generating a multitude of highly uncorrelated speckle patterns in a typical residential environment at a single frequency. We use a DMA implemented as an electrically-large cavity excited by a single port and loaded with many individually-addressable tunable metamaterial radiators. We placed such a DMA in one corner of a plywood-walled L-shape room transmitting microwave signals at 19 GHz as we changed the tuning states of the metamaterial radiators. In another corner, in the non-line-of-sight of the DMA, we conducted a scan of the field generated by the DMA. For comparison, we also performed a similar test where the DMA was replaced by a simple dipole antenna with fixed pattern but generating a signal that spanned 19 - 24GHz. Using singular value decomposition of the scanned data, we demonstrate that the DMA can generate a multitude of highly uncorrelated speckle patterns at a single frequency. In contrast, a dipole antenna with a fixed pattern can only generate such a highly uncorrelated set of patterns when operating over a large bandwidth. The experimental results of this paper suggest that DMAs can be used to capture a diversity of information at a single frequency which can be used for single frequency computational imaging systems, NLOS motion detection, gesture recognition systems, and more.

We present a ray-tracing analysis of a smart motion detector based on a dynamically reconfigurable metasurface antenna (DMA). A DMA consists of an array of metamaterial radiators excited by a single-port waveguide or cavity. By incorporating simple switchable components into each element and addressing them individually, DMAs can generate a myriad of spatially distinct radiation patterns and alter them as a function of an applied voltage. These patterns have the potential to probe all regions of a room or set of rooms and detect motion, even when operating over an extremely narrow bandwidth. Through the acquisition of time-resolved measurements, the DMA sensor can retrieve temporal signatures and distinguish between different sources of movements. We investigate this sensing paradigm using a ray tracing simulation. We first replicate the trends obtained from recent experiments using our simulation platform to ensure that numerical ray tracing generates data that is a faithful representation of the real-life physics. We then demonstrate that temporal signals obtained in this manner carry information about the nature of the movement. Specifically, by using power spectra and filtering, we are able to extract features that correspond to specific motion patterns. These results constitute the first step toward incorporating DMAs into a smart sensor equipped with learning algorithms that can distinguish between human and non-human motion with high fidelity.

We present the design and experimental demonstration of a cavity-backed, holographic metasurface capable of focusing microwaves in the Fresnel zone. The circular cavity consists of two stacked plates: microwaves are injected into the bottom plate via a coaxial connector, which forms the feed layer, and are coupled to the top holographic metasurface layer via an annular ring on the periphery of the cavity. This coupling results in an inward traveling cylindrical wave in the top layer, which serves as the reference wave for the hologram. A sparse array of slots, patterned into the upper plate, constitutes the hologram that produces the focal spot. To mitigate high sidelobe levels and improve performance, a tapered design, facilitated by varying the slot size, is also introduced. The proposed designs-which have a 10-cm diameter, operate at 20 GHz, and form a focal spot at a distance of 10 cm-are validated using full-wave simulations as well as measurements of fabricated samples. The proposed focusing metasurface may find application as a compact source for Fresnel zone wireless power transfer and remote sensing schemes.

Abstract 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.

The correlation-based synthetic aperture radar imaging technique, termed radar coincidence imaging, is extended to a fully multistatic multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) configuration. Within this framework, we explore two distinct processing schemes: incoherent processing of intensity data, obtained using asynchronous receivers and inspired by optical ghost imaging works, and coherent processing with synchronized array elements. Improvement in resolution and image quality is demonstrated in both cases using numerical simulations that model an airborne MIMO SAR system at microwave frequencies. Finally, we explore methods for reducing measurement times and computational loads through compressive and gradient image reconstruction using phaseless data.