arXiv subjects

Philipp del Hougne

Publications and source records attributed to Philipp del Hougne.

At least 19 recordsLinked to original sources

arXiv2026

Möbius Reparametrization of Multiport-Network Models of PIN-Diode-Programmable Metasurfaces for Accurate Low-Order Neumann Approximations

Accurate models of programmable metasurfaces based on multiport-network theory (MNT) account for mutual coupling (MC) through a configuration-dependent matrix inversion. The latter's high computational cost during gradient-based optimization (GBO) can be alleviated via a finite-order Neumann approximation. We show that the accuracy of this approximation depends strongly on the (tacitly) chosen MNT parametrization. For 1-bit-programmable meta-elements (e.g., meta-elements based on PIN diodes), we identify a closed-form Möbius reparametrization that depends only on the meta-element's two load states and requires neither training data nor numerical optimization. For an experimentally estimated proxy MNT model of a fabricated 96-element 19-GHz dynamic metasurface antenna with strong MC, a second-order Neumann approximation with our reparametrization achieves forward and control-gradient accuracies of 21.68 and 21.70 dB, respectively. Relative to the full proxy MNT, it reduces the runtime of a combined forward and control-gradient evaluation by a third and the saved-tensor memory by a factor of ten. In a prototypical GBO problem of end-to-end optimization for DMA-based scene classification, it achieves 94.97% test accuracy vs. 95.57% with the full model. We further note that MC-strength metrics and MC-unaware benchmarks should be parametrization-invariant, motivating, for instance, definitions based on the best directly fitted zeroth-order model.

eess.SP↗

arXiv2026

Wave-Domain Semantic Equalization Using a Practical Dynamic Metasurface Antenna with Strong Mutual Coupling

Semantic mismatch between independently trained AI-native agents in heterogeneous networks can impair semantic communications. Hybrid analog-digital semantic equalization can align the incompatible latent representations without retraining the semantic transceivers. We study a practical realization of this approach based on a fabricated dynamic metasurface antenna (DMA), an emerging low-cost, low-power, ultracompact technology for hybrid analog-digital beamforming. We model the DMA-assisted channel using multiport-network theory (MNT), accounting for mutual coupling (MC), structural scattering, and binary lossy tuning states. We use the experimentally estimated MNT parameters of our fabricated 19-GHz DMA prototype with strong MC. We jointly optimize digital pre- and post-equalizers and the DMA at a reference receiver geometry for latent-space alignment, then freeze the digital stages and adapt only the DMA after receiver motion. In our CIFAR-10 image-classification task at the receiver, the jointly optimized system achieves 94.5% accuracy, while DMA-only adaptation restores a median accuracy of 90.3% after receiver motion. Our semantic-aware wave-domain adaptation substantially outperforms semantic-unaware benchmarks. We further observe that varying the DMA configuration across channel uses provides little additional benefit for either semantic-aware or semantic-unaware optimization.

eess.SP↗

arXiv2026

Bounds on Shaping Partially Coherent Microwaves with Programmable Scattering Systems

We derive bounds on manipulating partially coherent microwaves with programmable scattering systems. We first consider the concentration of power from a partially coherent input into a single output port and derive a prototype-aware bound by combining multiport network theory (MNT) with semidefinite relaxation (SDR). We then address general coherency-matrix synthesis, deriving architecture-independent bounds on fidelity and useful strength, prototype-specific scalar refinements thereof, and fully prototype-aware SDR bounds on the useful-strength--fidelity Pareto frontier. The prototype-aware formulations account for mutual coupling, loss, discrete tunability, and static scattering. We evaluate the bounds on four experimental RIS-parametrized MIMO systems with up to 100 1-bit-programmable elements, using proxy-MNT models estimated from measurements. The resulting bounds are generally tight compared with feasible discrete-optimization outcomes. Interestingly, for coherency synthesis, the simpler bounds can in some cases be tighter than the fully prototype-aware bounds, highlighting their complementarity. Our results provide certified limits for wave-domain processing and harvesting of partially coherent microwaves.

physics.app-ph↗

arXiv2026

Coherent Control of Wave Scattering via Minimal-Parameter Tuning of Complex Spectra

We introduce and validate a theoretical framework for coherent control of multichannel linear scattering to route waves through complex geometries with multiple scattering. We show that steady-state perfect routing solutions are achievable at any frequency via tuning geometric parameters so that multiple complex eigenfrequencies coincide on the real axis. The relevant complex spectra describe critically constrained scattering processes (CCONs), where a specific number of generically accessible outgoing channels are inaccessible due to destructive interference. Focusing on electromagnetic waves, we demonstrate in simulations routing and demultiplexing with high discrimination of signals in a multiport chaotic cavity with a small number of tunable scatterers and free-space signal routing in a grating coupler with a similar number of tunable elements in its unit cell. The minimal number of tuning parameters required is predicted by codimension arguments and validated in simulations. A special class of perfect reflection processes are found to be enhanced in the presence of time-reversal symmetry. A similar approach can be used to implement other interesting functionalities, such as isolation, power division, mode conversion and filtering. The method can be applied to other classical waves and also to quantum matter waves.

physics.optics↗

arXiv2026

RIS-Aided Wireless Multiport Sensing: Multiplexed De-Embedding of an RIS-Programmable Over-the-Air Fixture

Wireless multiport sensing aims to remotely retrieve the scattering matrix of a multiport device under test (DUT) connected to not-directly-accessible (NDA) antennas, based on scattering parameters measured with remotely located accessible antennas that couple over the air (OTA) to the NDA antennas. A central bottleneck is that the required number of accessible antennas grows with the number of unknowns in the DUT's scattering matrix. Here, we address this bottleneck by using a reconfigurable intelligent surface (RIS) to generate measurement diversity during the DUT characterization. We interpret the setup as measuring the DUT via an RIS-programmable OTA fixture. We first characterize the RIS-programmable OTA fixture using a specific known tunable load network to terminate the NDA antennas. Then, we reconstruct the DUT's scattering matrix by multiplexed de-embedding of the RIS-programmable OTA fixture, using measurements of the DUT acquired across multiple RIS configurations. Based on our system model formulated in terms of multiport-network theory, we quantify and maximize the diversity of our measurement sequence by optimizing the deployed ensemble of RIS configurations. We validate our approach experimentally at 2.45 GHz in a rich-scattering radio environment. We systematically examine the influence of the number of accessible antennas, the number of RIS configurations, and the optimization of the RIS configurations. For a SISO link (two accessible antennas), optimized RIS configurations reduce the median reconstruction mean-squared error for 100 DUT measurements by 45% from $1.57{\times}10^{-3}$ to $8.59{\times}10^{-4}$. These results demonstrate that RIS diversity can compensate for a limited number of accessible antennas, paving the way toward low-cost wireless multiport sensing for industrial, biomedical, and smart-environment RFID systems.

eess.SP↗

arXiv2026

Non-Reciprocal Dynamic Metasurface Antenna: Practical Multiport-Network Modeling and Optimization for Multi-User Interference Resilience

Channel reciprocity fundamentally limits full-duplex (FD) base stations due to multi-user co-channel interference. We examine the potential of deploying a non-reciprocal dynamic metasurface antenna (NR-DMA) at the base station to overcome this limitation. Our NR-DMA architecture connects a single circulator to three feed ports of a multi-feed DMA with strong mutual coupling (MC) between its seven feeds and 96 1-bit-programmable meta-elements. We model our system with multiport network theory, using experimentally estimated proxy parameters of a fabricated 19-GHz DMA and the measured circulator response. Our NR-DMA's reconfigurability is captured by a diagonal tunable scattering matrix, showing that non-reciprocal DMAs and RISs need not require a "beyond-diagonal" tunable scattering matrix. We jointly optimize the DMA state, analog feed weights, circulator-port assignment, and circulation direction. Our optimized NR-DMA realizes distinct forward and reverse channel responses. In our interference-limited high-SNR case study, the NR-DMA improves the FD sum rate by about 60% over a reciprocal DMA benchmark. Comparisons with proxy objectives and MC-unaware optimization show that end-to-end FD optimization and MC-aware modeling are both essential.

eess.SP↗

arXiv2026

ADC-Aware End-to-End Optimization of a Dynamic Metasurface Antenna with Strong Mutual Coupling for Monostatic Scene Classification

Dynamic metasurface antennas (DMAs) enable programmable wave-domain signal processing that can be jointly optimized with downstream digital processing in an end-to-end manner. Existing studies, however, typically assume ideal analog-to-digital conversion (ADC) and often rely on simplified electromagnetic models. Here, we study ADC-aware end-to-end optimization of a monostatic sensing pipeline based on a DMA with strong mutual coupling (MC). We model the wave domain using an MC-aware multiport-network model whose parameters were experimentally estimated for a fabricated chaotic-cavity-backed DMA with 96 one-bit-programmable meta-elements. We perform ADC-aware end-to-end optimization of the DMA configurations and digital classifier, either with awareness of a fixed uniform ADC or, optionally, with jointly learned ADC decision thresholds, and compare against baselines that assume an ideal ADC and/or ignore MC. Our results show that ADC awareness is essential in low-resolution ADC regimes: with one-bit ADCs and eight DMA configurations, deploying an ideal-ADC-trained system with a uniform one-bit ADC reduces the test accuracy from 95.5% to 56.0%, whereas ADC-aware training with the same fixed uniform one-bit ADC achieves 87.2%. We also show that without MC awareness the accuracy drops to the random-guess level. Learning non-uniform ADC thresholds provides at most modest additional gains over fixed uniform ADCs in the considered DMA-based sensing pipeline.

eess.SP↗

arXiv2026

Prototype-Aware Fundamental Electromagnetic Limits on Wavefront Synthesis with Programmable Metasurfaces

Wavefront synthesis is a central objective in many applications of programmable metasurfaces (PMs), ranging from electromagnetic holography and computational imaging to massive backscatter communications. Yet, fundamental limits on the ability of a given real-world PM prototype to synthesize a desired output wavefront remain largely unknown. Here, we derive prototype-aware and electromagnetically consistent bounds on target-wavefront synthesis in reconfigurable MIMO wave systems whose programmability stems from tunable lumped elements. Our approach combines multiport network theory (MNT), experimentally estimated proxy MNT parameters, and semidefinite relaxation. We account for relevant practical aspects of typical real-world PMs, such as mutual coupling, binary programmability, and lossy tunable loads. We derive bounds on strength-agnostic wavefront-synthesis fidelity, shape-agnostic target-mode strength, and the strength--fidelity Pareto frontier using two complementary threshold sweeps. We evaluate these bounds for four experimental MIMO systems whose transfer functions are parametrized by a reconfigurable intelligent surface (RIS), involving up to 100 1-bit-programmable elements and radio environments ranging from rich scattering to free space. Our bounds yield practical insights such as the identification of unattainable performance regions and the close-to-optimality certification of certain optimization outcomes. Comparisons with feasible discrete-optimization benchmarks show that the bounds can often be closely approached in practice, indicating tightness. While demonstrated with a RIS prototype, our methodology applies broadly to lumped-element-reconfigurable wave systems, including dynamic metasurface antennas. Altogether, this work contributes to the development of a prototype-aware electromagnetic information theory for reconfigurable wave systems.

eess.SP↗

arXiv2026

Single-Frequency Symmetry-Empowered Through-Barrier Sensing in Reconfigurable Complex Media

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.

physics.optics↗

arXiv2026

Cross-Harmonic Ambiguity-Aligned Multiport Parameter Estimation for Time-Floquet RIS

A time-Floquet reconfigurable intelligent surface (TF-RIS) periodically modulates its elements within a signaling interval, enabling frequency conversion and additional degrees of freedom compared with a conventional RIS. Time-Floquet multiport-network theory (TF-MNT) provides a physics-consistent model for TF-RISs that accounts for inter-element coupling, but its practical use requires estimating the underlying parameters when the TF-RIS design and radio environment are (partially) unknown. In this Letter, we propose a segmented estimation approach for constructing an accurate proxy TF-MNT model from end-to-end measurements. First, with the TF-RIS operated as a conventional RIS, we estimate conventional proxy MNT parameters independently at each considered time-Floquet harmonic. Second, under periodic time modulation, we align the inherent ambiguities among the per-harmonic conventional proxy MNT parameters, considering three measurement setups with different access to phase and harmonic information. Based on full-wave numerical simulations, we quantify the impact of the number of measurements and the noise level on the proxy-model accuracy. Finally, we demonstrate the performance loss incurred without the proposed ambiguity alignment in a canonical harmonic backscatter communications scenario.

eess.SP↗

arXiv2026

Symmetry-Empowered Through-Barrier Sensing in Complex Media

Symmetry strongly impacts wave transport in complex media. In this Letter, we demonstrate that the phenomenon of symmetry-induced through-barrier transmission enhancement enables quantitative sensing across barriers in complex media. We consider two mirror-symmetric chaotic cavities coupled through a narrow slit and containing point scatterers at mirror-symmetric positions. The characteristics of the scatterers in one cavity are unknown, whereas those of the scatterers in the other cavity are programmable. By tuning the programmable scatterers to maximize broadband total transmission, we recover the unknown scatterers' characteristics across the barrier. We show that reliable sensing requires a sufficiently large bandwidth, because otherwise a narrowband asymmetric resonant enhancement can dominate over the desired symmetry-induced enhancement. We further examine how absorption and barrier opacity influence the minimum required bandwidth. Our results establish a symmetry-empowered principle for through-barrier sensing in complex media, suggesting a route toward through-wall imaging in complex environments.

physics.optics↗

arXiv2026

Optimizing Dynamic Metasurface Antenna Configurations for Direction-of-Arrival and Polarization Estimation Using an Experimentally Calibrated Multiport-Network Model

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.

eess.SP↗

arXiv2026

Mutual-Coupling-Aware Optimization of a Time-Floquet RIS for Harmonic Backscatter Communications

This Letter studies the optimization of a wireless communications system empowered by a periodically time-modulated reconfigurable intelligent surface, coined time-Floquet RIS (TF-RIS), in the presence of mutual coupling (MC) among the RIS elements. In contrast to a conventional RIS whose elements may be reconfigured between signaling intervals, a TF-RIS periodically modulates its elements within a signaling interval, thereby inducing frequency conversion. Periodic time modulation is particularly attractive for harmonic backscatter communications to avoid self-jamming. Based on time-Floquet multiport network theory, we formulate an MC-aware optimization problem for binary-amplitude-shift-keying (BASK) harmonic backscatter communications with practical 1-bit-programmable TF-RIS elements. We propose a general discrete-optimization algorithm and evaluate its performance based on realistic model parameters. We systematically examine the performance dependence on the time resolution of the periodic modulation and the number of retained harmonics. Benchmarking against an MC-unaware approach reveals the importance of MC awareness for the more challenging optimization problem of simultaneous desired-harmonic-channel-gain maximization and undesired-harmonic-channel-gain minimization.

eess.SP↗

arXiv2026

Channel Estimation via Tensor Decomposition for Dynamic Metasurface Antennas with Known Mutual Coupling: Algorithms and Experiments

Dynamic metasurface antennas (DMAs) are an emerging hybrid-MIMO technology distinguished by an ultrathin form factor, low cost, and low power consumption. In real-world DMA prototypes, mutual coupling (MC) between meta-elements is generally non-negligible; some architectures even deliberately exploit strong MC to enhance wave-domain flexibility. In this paper, we address channel estimation (CE) for DMAs with known MC by formulating it as a tensor-decomposition problem. We develop a generalized block Tucker alternating least squares (BTALS) algorithm, together with specialized variants for cases with known direct and/or feed channel. We also develop a reciprocity-aware bilinear factorization method for the case with known direct channel. We experimentally validate our algorithms using an 18 GHz DMA prototype whose 7 feeds and 96 meta-elements are strongly coupled via a chaotic cavity. Our general BTALS algorithm reaches an accuracy of 43.1 dB, only 0.3 dB below the upper bound imposed by experimental noise. All proposed algorithms generally outperform the prior-art reference scheme thanks to the superior noise rejection enabled by the tensor-based framework. We further study the minimum number of required measurements as a function of the number of feeds and demonstrate the importance of MC awareness by comparison with an MC-unaware benchmark. Finally, we apply BTALS to a second setup enabling the prediction of the DMA's full dual-polarization 3D radiation diagram. We also measure the latter for DMA configurations optimized for channel-gain enhancement based on the estimated channels. Altogether, our work establishes the practical relevance of MC-aware tensor methods; beyond DMAs, it applies to all wireless systems with wave-domain programmability enabled by tunable lumped elements.

eess.SP↗

arXiv2026

Expressivity of Programmable-Metasurface-Based Physical Neural Networks: Encoding Non-Linearity, Structural Non-Linearity, and Depth

Wave-based signal processing conventionally encodes input data into the input wavefront, making it challenging to implement non-linear operations. Programmable wave systems enable an alternative approach: encoding the input data into the scattering properties of tunable components. With such structural input encoding, two potentially non-linear mappings are involved: first, from the input data to the tunable components' scattering characteristics, and, second, from these scattering characteristics to the output wavefront. In this paper, we systematically examine the expressivity of a wave-based physical neural network (WPNN) with structural input encoding. Our analysis is based on a physics-consistent multiport-network model of a compact D-band rich-scattering cavity parametrized by a 100-element programmable metasurface. We separately control encoding non-linearity, structural non-linearity, and network depth in order to examine their interplay, considering a controlled scalar regression task. With phase encoding and strong inter-element mutual coupling (MC), both aforementioned mappings are strongly non-linear and the WPNN performs very well even with a single layer. We further observe that additional layers can partially compensate for weak inter-element MC. In addition, we demonstrate that WPNN depth can improve expressivity even when it is not associated with an increase in trainable weights. Altogether, our results provide a physics-consistent picture of how encoding choice, MC strength, and depth jointly govern the expressive power of PM-based WPNNs, informing design choices for future experimental implementations of WPNNs.

eess.SP↗

arXiv2026

Electromagnetic Bounds on Realizing Targeted MIMO Transfer Functions in Real-World Systems with Wave-Domain Programmability

A key question for most applications involving reconfigurable linear wave systems is how accurately a desired linear operator can be realized by configuring the system's tunable elements. The relevance of this question spans from hybrid-MIMO analog combiners via computational meta-imagers to programmable wave-domain signal processing. Yet, no electromagnetically consistent bounds have been derived for the fidelity with which a desired operator can be realized in a real-world reconfigurable wave system. Here, we derive such bounds based on an electromagnetically consistent multiport-network model (capturing mutual coupling between tunable elements) and accounting for real-world hardware constraints (lossy, 1-bit-programmable elements). Specifically, we formulate the operator-synthesis task as a quadratically constrained fractional-quadratic problem and compute rigorous fidelity upper bounds based on semidefinite relaxation. We apply our technique to three distinct experimental setups. The first two setups are, respectively, a free-space and a rich-scattering $4\times 4$ MIMO channel at 2.45 GHz parameterized by a reconfigurable intelligent surface (RIS) comprising 100 1-bit-programmable elements. The third setup is a $4\times 4$ MIMO channel at 19 GHz from four feeds of a dynamic metasurface antenna (DMA) to four users. We systematically study how the achievable fidelity scales with the number of tunable elements, and we probe the tightness of our bounds by trying to find optimized configurations approaching the bounds with standard discrete-optimization techniques. We observe a strong influence of the coupling strength between tunable elements on our fidelity bound. For the two RIS-based setups, our bound attests to insufficient wave-domain flexibility for the considered operator synthesis.

eess.SP↗

arXiv2026

Electromagnetically Consistent Bounds on Information Transfer in Real-World RIS-Parametrized Wireless Channels

A reconfigurable intelligent surface (RIS) endows a wireless channel with programmability that can be leveraged to optimize wireless information transfer. While many works study algorithms for optimizing such a programmable channel, relatively little is known about fundamental bounds on the achievable information transfer. In particular, non-trivial bounds that are both electromagnetically consistent (e.g., aware of mutual coupling) and in line with realistic hardware constraints (e.g., few-bit-programmable, potentially lossy loads) are missing. Here, based on a rigorous multiport network model of a single-input single-output (SISO) channel parametrized by 1-bit-programmable RIS elements, we apply a semidefinite relaxation (SDR) to derive a fundamental bound on the achievable SISO channel gain enhancement. A bound on the maximum achievable rate of information transfer at a given noise level follows directly from Shannon's theorem. We apply our bound to several numerical and experimental examples of different RIS-parametrized radio environments. Compared to electromagnetically consistent benchmark bounding strategies (a norm-inequality bound and, where applicable, a relaxation to an idealized beyond-diagonal load network for which a global solution exists), we consistently observe that our SDR-based bound is notably tighter. We reach at least 64 % (but often 100 %) of our SDR-based bound with standard discrete optimization techniques. The applicability of our bound to concrete experimental systems makes it valuable to inform wireless practitioners, e.g., to evaluate RIS hardware design choices and algorithms to optimize the RIS configuration. Our work contributes to the development of an electromagnetic information theory for RIS-parametrized channels as well as other programmable wave systems such as dynamic metasurface antennas or real-life beyond-diagonal RISs.

eess.SP↗

arXiv2026

Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS

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.

eess.SP↗