Philipp del Hougne

Mirror symmetry can strongly enhance the transmission of waves through a barrier inside a complex medium. We recently showed that this phenomenon enables quantitative through-barrier sensing: by tuning programmable scatterers on one side of the barrier to maximize the broadband total transmission through the barrier, the characteristics of scatterers at mirror-symmetric positions on the other side of the barrier can be determined. Considering a sufficiently large bandwidth was crucial to ensure that no accidental narrowband asymmetric resonance can outperform the symmetry-induced transmission enhancement. Here, we overcome this scheme’s vexing need for a large bandwidth by replacing the underlying frequency diversity with configurational diversity. Specifically, we introduce auxiliary tunable scatterers at mirror-symmetric positions on either side of the barrier and sweep their characteristics through a series of random mirror-symmetric configurations. We tune the programmable main scatterers on one side of the barrier to maximize the average of the total through-barrier transmission over a series of configurations of the auxiliary scatterers at a single frequency , in order to sense the characteristics of the main scatterers on the other side of the barrier. We systematically study the accuracy of our single-frequency sensing scheme based on a multiport-network system model that cascades two mirror-related wave-chaotic cavities with a weakly transmitting barrier in between. We further examine an extension to non-reciprocal chaotic cavities involving circulators. Altogether, our results establish configurational diversity as a route to single-frequency, symmetry-empowered through-barrier sensing in reconfigurable complex media.

Sensing the direction of arrival and polarization of impinging signals is a key prerequisite for beamforming and interference mitigation in modern wireless communication systems. Dynamic metasurface antennas (DMAs) can multiplex direction- and polarization-dependent field information onto a single detector by sequentially switching between programmable configurations. This makes DMAs attractive for joint direction-of-arrival and polarization (DoA-P) estimation with a single radio-frequency chain. Experimental demonstrations have so far relied on random pre-measured configuration sequences because optimizing the configurations requires an accurate forward model of the fabricated DMA. Here, we use an experimentally calibrated model based on multiport-network theory (MNT) to optimize DMA configuration sequences for DoA-P estimation. Our experimentally calibrated MNT model predicts the dual-polarized far-field response of our 96-element DMA for arbitrary admissible configurations, enabling model-based optimization without additional radiation-pattern measurements. We optimize sequences using effective-rank-based surrogate objectives and compare them with random sequences as a function of the sequence length and the noise level. The optimized sequences yield the largest gains in the intermediate-SNR and intermediate-sequence-length regime, where the inverse problem is neither noise-limited nor already solved by random diversity. We also tackle a dual-source scenario involving a jammer and a desired transmitter. Our results illustrate some of the potential in the context of jamming-resilient communications that is unlocked by experimentally calibrated MNT models for fabricated DMAs.

Wireless multiport sensing remotely retrieves the scattering matrix of a multi-port device under test (DUT) connected to a set of not-directly-accessible (NDA) antennas that couple over-the-air (OTA) to a set of accessible antennas. If (i) the OTA fixture characteristics are known, and (ii) the number of independent measurements at the accessible antennas is sufficient, the OTA fixture can be de-embedded to recover the DUT characteristics. In recent prior work, we solved (i) by connecting the NDA antennas to a specific known tunable load network (TLN). Here, we tackle (ii) by additionally using the TLN to provide measurement diversity. The connection between OTA fixture and TLN constitutes a programmable fixture (PF). When the DUT characteristics cannot be identified based on a single PF realization, we add measurement diversity with multiple PF realizations. The underlying “multiplexed de-embedding” achieves the joint de-embedding of an ensemble of PF realizations when a single PF realization cannot be de-embedded. We experimentally demonstrate our concept by remotely estimating the scattering matrix of a reciprocal, passive, linear 4-port DUT (10 complex-valued unknowns) via a rich-scattering OTA fixture purely based on measurements of a single transmission coefficient between two accessible antennas across 30 different PF realizations. Our PF diversity reduces the average mean-squared error (MSE) by an order of magnitude, from 0.143 to 0.009. We systematically study the trade-off between the number of independent measurements at the accessible antennas and the number of PF realizations. Multiplexed de-embedding of the OTA fixture paves the way for implementing wireless multiport sensing with low hardware complexity in applications such as industrial and medical RFID.

The inverse design of electrically large wave devices often leverages reduced‐order multiport models combined with discrete optimization, which requires repeated evaluations of complex interconnections of multiport subsystems that differ only in a few constituent blocks. Rather than reevaluating each configuration from scratch, this work introduces a computationally efficient method that reuses previous evaluations via low‐rank updates based on the Woodbury matrix identity. A closed‐form framework for arbitrarily complex connection schemes of multiport networks is formulated. Unified equivalence principles for interpretations of subsystem connections are presented and leveraged to reduce the computational burden of the closed‐form approach. Moreover, a closed‐form expression for the power waves travelling through connected ports is derived. The method is demonstrated on a meta‐network with serial, parallel, and cyclic connections among multiport subsystems, and validated with physics‐compliant analytic calculations on graph‐based subsystems. Exhaustive statistical analyses quantify the computational benefits arising from reducibility and updatability across problem sizes. Finally, it is shown that working with scattering parameters, rather than impedance or admittance parameters, confers a fundamental numerical advantage for an important class of connection schemes whose closed‐form analysis treats some links as delayless, lossless, reflectionless, reciprocal two‐port scattering systems.

Most use cases of reconfigurable antennas require an accurate forward model mapping the configuration to the radiated field (and reflections at feeds). Emerging dynamic metasurface antennas (DMAs) confront the conventional approach of extracting such a model from a numerical simulation with multiple challenges. First, the cost of accurately simulating an intricate and electrically large DMA architecture might be prohibitive. Second, the model-reality mismatch due to fabrication inaccuracies might be substantial, especially at higher frequencies and for DMA architectures leveraging strong interelement mutual coupling (MC) to maximize their tunability. These considerations motivate an experimental parameter estimation for DMA forward models. The main challenge lies in the forward model’s non-linearity due to inter-element MC. Multiport network theory (MNT) can accurately capture MC but the MC parameters cannot be measured directly. In this article, we demonstrate the experimental estimation of a high-accuracy proxy MNT model for a 19-GHz DMA with 7 feeds and 96 elements, where all feeds and elements are strongly coupled via a chaotic cavity. For a given DMA configuration and excitation, our proxy MNT model predicts the reflected field at the feeds and the radiated field with accuracies of 40.3 dB and 37.7 dB, respectively. A simpler, MC-unaware benchmark model only achieves 2.6 dB and 3.3 dB, respectively. We systematically examine the influence of the number of feeds and measured DMA configurations on the model accuracy, motivating the inclusion of “auxiliary calibration feeds” to facilitate the parameter estimation when the intended DMA operation is limited to a single feed. Finally, we measure DMA configurations optimized based on our proxy MNT model.

While the quantification of the multiplexing gain in a static linear multiple-input multiple-output (MIMO) system in terms of its effective electromagnetic degrees of freedom (EEMDOFs) is well established, the counterpart for a backscatter MIMO (BS-MIMO) system is so far missing. A BS-MIMO system encodes the input information into the loads of backscatter elements. Due to mutual coupling, the mapping from load configuration to observed fields is fundamentally non-linear, which complicates the analysis of BS-EEMDOFs. We introduce a definition of BS-EEMDOFs based on the Jacobian of the observed fields with respect to the load configuration. We derive a closed-form expression from multiport network theory which demonstrates that the number of BS-EEMDOFs is fundamentally a random variable, whose distribution depends on the mutual coupling between the backscatter elements, the considered distribution of load configurations, and the coherent illumination. The modes associated with BS-EEMDOFs lie in the column space of the end-to-end channel matrix from backscatter array ports to receiver ports, but the number of BS-EEMDOFs is generally different from the number of benchmark EEMDOFs associated with the same array being coherently fed rather than tunably terminated. The dependence on the coherent illumination yields optimized coherent illumination as a control knob for the number of BS-EEMDOFs. We present numerical and experimental results for the evaluation and optimization of the number of BS-EEMDOFs in different radio environments with large reconfigurable intelligent surfaces, including an example in which the mean of the number of BS-EEMDOFs (with optimized coherent illumination) exceeds the number of conventional EEMDOFs. Our work provides an electromagnetically consistent way of quantifying and optimizing the multiplexing gain in BS-MIMO systems.

Physics-consistent optimization of reconfigurable intelligent surfaces (RISs) is thwarted in practice by the difficulty of experimentally estimating the mutual coupling (MC) between RIS elements. For large RISs, experimental MC estimation is fundamentally challenging because of the quadratic scaling of the number of unknowns with the number of RIS elements. In this Letter, we present a generic and flexible reduced-rank MC representation that allows wireless practitioners to choose a trade-off between model complexity and accuracy. We experimentally validate the direct reduced-rank MC estimation for a 100-element RIS in three radio environments (rich scattering, attenuated scattering, free space). We observe a strong environmental dependence of the influence of rank reduction on accuracy. Model-based performance evaluations highlight that the importance of MC awareness in optimization depends strongly on the radio environment.

Future wireless networks must achieve large gains in data rate, energy efficiency and latency while integrating sensing and computation. Programmable wave-domain computing (pWDC), which processes signals directly through reconfigurable wavematter interactions, offers a way to offload part of this burden from electronic processors. In this Perspective, we review historical roots and recent work on pWDC and discuss its promise and challenges. We focus on three challenges: prototype-aware runtime optimization of pWDC hardware, enriching functionalities beyond linear continuouswave operation, and integrating pWDC into network-level resource management. Finally, we outline open questions regarding expressivity, practicality, and security that will arise in the transition of pWDC to real-life deployment in future wireless infrastructures.

This Letter addresses the physics-consistent optimization of reconfigurable intelligent surfaces (RISs) with mutual coupling (MC) and 1-bit-programmable RIS elements. This combination of constraints is typical of current prototypes but unexplored in theoretical work. First, we present a simple statistical generator for multiport-network-theory (MNT) parameters of rich-scattering, RIS-parametrized channels. We account for reciprocity, passivity, and coherent backscattering; then, we add a simple hyper-parameter to control the MC strength. Second, we benchmark model-agnostic (dictionary search, coordinate descent, genetic algorithm) and model-based (temperature-annealed back-propagation) strategies under varying MC, with and without intelligent initialization. Except when MC is negligible, coordinate descent with random initialization offers the best trade-off in performance, runtime, and memory. Our insights can guide wireless practitioners who optimize RIS prototypes and other reconfigurable wave systems.