Understanding beamshaping in reconfigurable intelligent surfaces (RIS) is crucial for practical deployment, especially with the recent emphasis on self-configuring RISs. However, accurate beamshaping must account for factors such as mutual coupling and structural scattering. This paper proposes a beamshaping framework for physically consistent RISs using a modified signal model that bridges communication theory and multiport network theory. By focusing on desired beam and null locations, we enable pre-calculation of RIS channel gain matrices, reducing computational complexity. The optimization problems are then solved using constrained simulated annealing (CSA). Numerical simulations validate the framework by demonstrating wide beam and null formation and the necessity of discrete optimization for accurate beamshaping with less than 7 bits of discrete control. Results also reveal that ignoring mutual coupling in structural scattering leads to a notable decline in null quality.

Radio frequency wireless power transfer enables charging low-power mobile devices without relying on wired infrastructures. Current existing wireless power transfer systems are typically designed assuming far-field propagation, where the radiated energy is steered to towards given angles, resulting in limited efficiency and possible radiation in undesired locations. An emerging technology for wireless signaling is based on dynamic metasurface antennas (DMAs), which efficiently realize electrically large arrays. When such arrays are employed at high frequencies, wireless power transfer might take place in the radiating near-field (Fresnel) region, where spherical wave propagation holds, providing more degrees-of-freedom and improved performance. In this article, we study wireless power transfer systems charging multiple devices in the Fresnel region, where the energy transmitter is equipped with a DMA, exploring how the antenna configuration can exploit the spherical wavefront to generate focused energy beams. In particular, after presenting a mathematical model for DMA-based radiating near-field wireless power transfer systems, we characterize the weighted sum-harvested energy maximization problem of the considered system, and we propose an efficient solution to jointly design the DMA weights and digital precoding vector. Then, by accounting for hardware constraints, we further extend our study to encompass practical scenarios with discrete phase shifts in DMA elements. Simulation results show that our design generates focused energy beams capable of improving energy transfer efficiency in the radiating near-field with minimal energy pollution.

Wireless networks-on-chip (WNoCs) are an enticing complementary interconnect technology for multi-core chips but face severe resource constraints. Being limited to simple on-off-keying modulation, the reverberant nature of the chip enclosure imposes limits on allowed modulation speeds in sight of inter-symbol interference, casting doubts on the competitiveness of WNoCs as interconnect technology. Fortunately, this vexing problem was recently overcome by parametrizing the on-chip radio environment with a reconfigurable intelligent surface (RIS). By suitably configuring the RIS, selected channel impulse responses (CIRs) can be tuned to be (almost) pulse-like despite rich scattering thanks to judiciously tailored multi-bounce path interferences. However, the exploration of this "over-the-air" (OTA) equalization is thwarted by (i) the overwhelming complexity of the propagation environment, and (ii) the non-linear dependence of the CIR on the RIS configuration, requiring a costly and lengthy full-wave simulation for every optimization step. Here, we show that a reduced-basis physics-compliant model for RIS-parametrized WNoCs can be calibrated with a single full-wave simulation. Thereby, we unlock the possibility of predicting the CIR for any RIS configuration almost instantaneously without any additional full-wave simulation. We leverage this new tool to systematically explore OTA equalization in RIS-parametrized WNoCs regarding the optimal choice of delay time for the RIS-shaped CIR’s peak. We also study the simultaneous optimization of multiple on-chip wireless links for broadcasting and conduct a performance evaluation in terms of the bit error rate. Looking forward, the introduced tools will enable the efficient exploration of various types of OTA analog computing in RIS-parametrized WNoCs.

The opening of the millimeter wave (mmWave) spectrum bands for 5G communications has motivated the need for novel spectrum sharing solutions at these high frequencies. In fact, reconfigurable intelligent surfaces (RISs) have recently emerged to enable spectrum sharing while enhancing the incumbents’ quality-of-service (QoS). Nonetheless, co-existence over mm Wave bands remains persistently challenging due to their unfavorable propagation characteristics. Hence, initiating mmWave spectrum sharing requires the RIS to further assist in improving the QoS over mmWave bands without jeopardizing spectrum sharing demands. In this paper, a novel simultaneous transmitting and reflecting RIS (STAR-RIS)-aided solution to enable mmWave spectrum sharing is proposed. In particular, the transmitting and reflecting abilities of the STAR-MS are leveraged to tackle the mmWave spectrum sharing and QoS requirements separately. The STAR-RIS-enabled spectrum sharing problem between a primary network (e.g. a radar transmit-receive pair) and a secondary network is formulated as an optimization problem whose goal is to maximize the downlink sum-rate over a secondary multiple-input-single-output (MISO) network, while limiting interference over a primary network. Moreover, the STAR-MS response coefficients and beamforming matrix in the secondary network are jointly optimized. To solve this non-convex problem, an alternating iterative algorithm is employed, where the STAR-RIS response coefficients and beamforming matrix are obtained using the successive convex approximation method. Simulation results show that the proposed solution outperforms conventional RIS schemes for mmWave spectrum sharing by achieving a 14.57% spectral efficiency gain.

Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key role in future wireless communications, enabling programmable radio propagation environments. They are usually considered as almost passive planar structures that operate as adjustable reflectors, giving rise to a multitude of implementation challenges, including the inherent difficulty in estimating the underlying wireless channels. In this paper, we focus on the recently conceived concept of Hybrid Reconfigurable Intelligent Surfaces (HRISs), which do not solely reflect the impinging waveform in a controllable fashion, but are also capable of sensing and processing an adjustable portion of it. We first present implementation details for this metasurface architecture and propose a convenient mathematical model for characterizing its dual operation. As an indicative application of HRISs in wireless communications, we formulate the individual channel estimation problem for the uplink of a multi-user HRIS-empowered communication system. Considering first a noise-free setting, we theoretically quantify the advantage of HRISs in notably reducing the amount of pilots needed for channel estimation, as compared to the case of purely reflective RISs. We then present closed-form expressions for the Mean-Squared Error (MSE) performance in estimating the individual channels at the HRISs and the base station for the noisy model. Based on these derivations, we propose an automatic differentiation-based first-order optimization approach to efficiently determine the HRIS phase and power splitting configurations for minimizing the weighted sum-MSE performance. Our numerical evaluations demonstrate that HRISs do not only enable the estimation of the individual channels in HRIS-empowered communication systems, but also improve the ability to recover the cascaded channel, as compared to existing methods using passive and reflective RISs.

Programmable radio environments parametrized by reconfigurable intelligent surfaces (RISs) are emerging as a new wireless communications paradigm, but currently used channel models for the design and analysis of signal-processing algorithms cannot include fading in a manner that is faithful to the underlying wave physics. To overcome this roadblock, we introduce a physics-based end-to-end model of RIS-parametrized wireless channels with adjustable fading (coined PhysFad) which is based on a first-principles coupled-dipole formalism. PhysFad naturally incorporates the notions of space and causality, dispersion (i.e., frequency selectivity) and the intertwinement of each RIS element’s phase and amplitude response, as well as any arising mutual coupling effects including long-range mesoscopic correlations. The latter are induced by reverberation and yield a highly nonlinear parametrization of wireless channels through RISs, a pivotal property which is to date completely overlooked. PhysFad offers the to-date missing tuning knob for physics-compliant adjustable fading. We thoroughly characterize PhysFad and demonstrate its capabilities for a prototypical problem of RIS-enabled over-the-air channel equalization in rich-scattering wireless communications. We also share a user-friendly version of our code to help the community transition towards physics-based models with adjustable fading.

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction.

Recently, dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing massive multiple-input multiple-output (MIMO) wireless systems. The usage of large arrays, jointly with higher transmitted frequencies, often results in the communicating devices operating in the near-field (Fresnel) region, thus requiring different considerations compared to traditional systems, assumed to operate in the far-field regime. In this paper, we study the potential of beam focusing, feasible in near-field operation, for multi-user MIMO systems, where the base station is equipped with a DMA. We introduce a mathematical model for DMA-based near-field MIMO communications. Then, we characterize the sum-rate maximization problem of the considered system, and propose an efficient solution to jointly design the DMA weights and digital precoding vector. Simulation results show that our design generates focused beams such that users residing at the same angular direction can communicate reliably without interfering, which is not achievable using conventional far-field beam steering.

We recently introduced the concept of reconfigurable Wireless Networks on Chips (r-WNoCs) for which an on-chip reconfigurable intelligent surface (RIS) endows the wireless on-chip propagation environment with programmability. In this work-in-progress report, we apply this idea to a single-input multiple-output (SIMO) context. Specifically, we demonstrate that using an on-chip RIS we can simultaneously shape multiple channel impulse responses (CIRs) such that they become essentially pulse-like despite rich scattering inside the chip enclosure. Pulse-like CIRs are essential to enable high-speed information exchange between different processors on the same chip with the simple on-off-keying modulation schemes envisaged for WNoCs.

Conventional radio-frequency (RF) sensing systems rely on either frequency diversity or spatial diversity to ensure high sensing accuracy. Such reliance introduces several practical limitations that hinder the pervasive deployment of existing solutions. To circumvent this prevalent reliance, we present MetaSense, a system that leverages antenna pattern diversity for fine-grained RF sensing. MetaSense incorporates the dynamic metasurface antenna (DMA) and the auxiliary-assisted ensemble multimask learning (AEMML) framework in its design. The DMA is a novel type of antenna that can provide a diverse set of uncorrelated radiation patterns in a low-cost and low-complexity manner. The AEMML is a quality-aware learning framework that can dynamically assess and aggregate the heterogeneous channel measurements from different antenna patterns to ensure high sensing accuracy. It also incorporates a transfer learning model that allows it to generalize to new sensing conditions with few training instances required. We prototype MetaSense and demonstrate its effectiveness on a writing motion recognition task using a custom-designed 2-D DMA. The results show that MetaSense achieves 92% to 98% accuracy in classifying ten miniature writing motions, outperforming a nontunable antenna by 20% in all scenarios. Moreover, when deployed in new sensing positions where limited training instances are available, MetaSense requires as few as five training instances per class to achieve over 90% accuracy.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) techniques plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We present a model for DMAs which accounts for the configurable frequency selective profile of its metamaterial elements, resulting in a spectrally flexible hybrid structure. We then exploit previous results in task-based quantization to show characterized the achievable OFDM recovery accuracy for a given DMA configuration in the presence of bit-constrained ADCs, and propose methods for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that by properly exploiting the spectral diversity of DMAs, notable performance gains are obtained over existing designs of conventional hybrid architectures, demonstrating the potential of DMAs for realizing high performance massive antenna arrays of reduced cost and power consumption.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose an algorithm for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals, and that its performance is comparable to receivers operating without bit limitations, while being significantly less costly and more power efficient.

Massive multiple-input multiple-output (MIMO) communications are the focus of considerable interest in recent years. While theoretical gains of such massive MIMO have been established, implementing MIMO systems with large-scale antenna arrays in practice is challenging. Among the practical difficulties associated with massive MIMO implementations 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. DMAs realize planar large-scale arrays of tunable antenna elements, and can adaptively incorporate compression and analog combining in the physical antenna structure, thus reducing 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 the resulting systems, and propose an algorithm for designing practical DMAs to approach these limits. Our numerical results indicate that the performance of practical DMA-based massive MIMO systems is comparable with ideal antenna arrays.

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.

We propose a spatial multiplexing system using reconfigurable cavity-backed metasurface antennas. The metasurface antennas consist of a printed cavity with dynamically tunable metamaterial radiators patterned on one side and fed by multiple radio frequency ports on the other side (each port representing one communication node), forming a shared aperture. By individual tuning of the radiators, the antennas can generate steerable, concurrent beams that can be adapted to the properties of multiple-input-multiple-output (MIMO) channels. In this paper, we present a 2 × 2 MIMO system with simulated metasurface antennas as transmit and receive antennas operating at 5.9 GHz. We demonstrate that the flexibility in beamforming supported by the metasurface antennas can be used to achieve low spatial correlation and high SNR gain in clustered MIMO channels, leading to a significant improvement of the channel capacity. Numerical studies show 2.36-fold, 2.11-fold enhancements of capacity in MIMO channels with one and two clusters, respectively, compared with an MIMO system consisting of linear dipoles. The MIMO system based on the metasurface antennas can be low cost, low profile, and low power. The metasurface antenna thus has potential applications in small cell networks requiring high data rate under bandwidth, energy, and cost constraints.

In this paper, we present a brief overview of near-field plates, which are nonperiodic grating-like surfaces/arrays that can focus electromagnetic field to subwavelength resolutions. The general properties of near-field plates are described, and the procedure used to design these devices is outlined. The design of two separate near-field plates is discussed in detail. One of the near-field plates produces a subwavelength line (1-D) focus while the other a spot (2-D) focus. Potential applications of near-field plates are also reviewed.