Papers

Many iterative algorithms rely on bootstrapping. A variable is updated using a second, frozen copy as a target, which is periodically replaced with the updated variable. Majorize-minimize and inexact proximal-point methods share this structure, as does temporal-difference (TD) learning. However, existing convergence guarantees for scenarios that combine sampled updates with targets refreshed only every $K$ steps rely on the specific structure of the update, such as linear approximation or gradient-based inner steps, and on uniformly bounded sampling error. We instead model the sampled update as a stochastic operator on the parameter space, which reduces the analysis to a contraction argument that needs no gradient structure and allows the sampling error to grow with the iterates. Within this framework, we derive a finite-time bound for i.i.d. samples and any target-update period $K$. We show that the iterates converge geometrically in root mean square to a ball around the fixed point, provided the sensitivity to the frozen target is smaller than the contraction slack of the inner map. Existing deterministic frozen-target contraction and stochastic-gradient-type bounds follow as special cases of our framework, and simulations of TD learning reproduce the predicted contraction rate and scaling of the error floor with the step size.

This letter investigates a non-uniform activation aided distributed generalized spatial modulation (DGSM) scheme in a downlink distributed multiple-input multiple-output (DMIMO) system formed by geographically separated transmission reception points (TRPs). Unlike conventional uniform-activation DGSM, the proposed scheme assigns adaptive activation probabilities to different TRP subsets, thereby jointly exploiting the data-domain transmission capability and the index-domain information carried by cooperative subset selection. We derive a mutual-information upper bound under non-uniform activation and formulate a joint activation-probability and power allocation problem subject to a long-term average power constraint. To solve this problem, we develop a semi-closed-form joint probability and power optimization (SC-JPPO) algorithm, where the optimal activation probabilities are shown to follow a Softmax function of the effective utilities. Simulation results demonstrate that the proposed scheme improves spectral efficiency under the same activation scale and power budget compared with existing benchmarks.

With the growing demand for privacy-preserving and occlusion-resilient human pose recognition (HPR), 5G channel state information (CSI) offers a promising contactless sensing modality by integrating communication and sensing capabilities. However, collecting large-scale synchronized CSI-pose pairs remains costly in practical 5G systems. To address this limitation, we propose StructFlow-HPR, a structured pose-conditioned flow matching framework for generative CSI augmentation. StructFlow-HPR learns a continuous latent transport process from Gaussian noise to real CSI representations under pose guidance, while preserving the receiver-frequency topology of CSI through a reconstruction-preserving autoencoder. A pose-conditioned Transformer is further designed to model the latent velocity field and generate pose-aligned CSI samples via ordinary differential equation sampling. Experiments on real-world 5G sensing data show that StructFlow-HPR can produce realistic CSI-pose pairs and improve downstream HPR performance under limited-data conditions.

As wireless systems evolve toward 6G and beyond, antenna apertures are increasingly expected to become more programmable, multifunctional, and responsive to diverse communication, sensing, and imaging requirements. Dynamic metasurface antennas (DMAs) offer a promising hardware pathway toward this vision by integrating electronically addressable metamaterial elements within microwave and millimeter-wave (mmWave) radiating apertures. Unlike conventional phased arrays relying on dedicated phase-shifting networks, DMAs manipulate guided waves in microwave structures through electronically controlled resonant elements. In this way, DMAs offer phase-shifter-less beam-steering, multibeam radiation, and programmable wavefront control. This article explores state-of-the-art DMA technology from a microwave-engineering perspective, spanning guided-wave excitation, reconfigurable resonant meta-elements, microwave tuning technologies, RF-digital integration, and FPGA-controlled beamforming. Closely related electronically steered metasurface antenna architectures are also discussed within a unified microwave-hardware framework. Recent advances illustrating how guided-wave DMA hardware and high-speed electronics facilitate programmable communication, sensing, imaging, and space-time functionalities are reviewed. Key microwave design challenges toward wideband, scalable, and highly integrated DMA apertures are highlighted. Finally, emerging opportunities in satellite communications, near-field connectivity, fixed wireless access, wave-domain computing, and integrated wireless functionalities are explored as future directions for DMA technology. This article provides a unified microwave engineering perspective on the design, practical implementation, and emerging capabilities of DMAs as programmable front-ends for next-generation wireless systems.

Network-controlled repeaters (NCRs) are emerg- ing as low-cost, band-selective active scatterers that can re- shape the wireless propagation environment without backhaul or tight phase synchronization. We study physical-layer design for repeater-assisted multiple-input multiple-output (RA-MIMO) networks, such as hardware impairments, gain and activation control, duplexing, channel-state information acquisition, and wideband delay effects. We then discuss integrated sensing and communication (ISAC), where repeaters can extend non- line-of-sight (NLoS) visibility, improve target observability, and enhance weak-echo detection. These gains, however, are limited by amplified noise, clutter, feedback, and calibration errors, motivating joint optimization and gain control frameworks for swarm repeater deployments within ISAC.

Enhancing the robustness of deployed networks against failures and disruptions is critical for reliable operation. This requires deciding which new links to install and how strongly to weight them under limited resources. We study this problem through the Kirchhoff index, or total effective resistance, a spectral measure of global connectivity. The resulting augmentation problem couples discrete candidate-edge selection with continuous weight allocation under heterogeneous per-unit deployment costs, a total budget, and an exact-cardinality constraint. For a fixed weighted base graph, this yields a mixed-integer formulation and a semidefinite relaxation whose optimum lower-bounds the mixed-integer optimum. We cast the relaxation as a cone program and solve it numerically using a homogeneous self-dual embedding and first-order operator splitting. Feasible discrete designs are recovered through rounding-and-repair procedures and assessed by a posteriori gap estimates relative to the numerical semidefinite program (SDP) benchmark. As a scalable alternative, we develop an exact-$k$, budget-feasible greedy heuristic built on rank-one Laplacian updates and biharmonic-distance caching, and interpret its progress through a Bellman value-to-go benchmark with a conservative spectral lower bound on the local policy ratio. Experiments on synthetic and real infrastructure networks across graph sizes, budgets, weight distributions, and cost regimes show that, under fixed budgets, distance-proportional costs limit the achievable resistance reduction and shift installed conductance toward shorter links relative to uniform per-unit costs.

Integrated sensing and communications (ISAC) systems offer a promising framework for beam tracking, which enhances channel awareness and improves communication reliability in mobile millimeter wave (mmWave) networks. Motivated by this, we propose a low-complexity, model-driven channel state information (CSI) framework with integrated channel prediction capability in ISAC-enabled systems. To reduce the overhead of CSI acquisition and uplink feedback, the proposed framework leverages predicted target sensing parameters to construct partial CSI from both line-of-sight (LoS) and detected non-line-of-sight (NLoS) components. Exploiting this partial CSI, we formulate a beamforming optimization problem that minimizes the worst targets' Cramér-Rao bound (CRB) under per-user communication constraints. This problem is non-convex and highly non-linear; therefore, we develop an efficient suboptimal solution using successive convex approximation (SCA) and Schur-complement-based decomposition techniques, and we evaluate its complexity in comparison with other approaches. Moreover, since obtaining the absolute phase of the channel paths may require additional pilot resources, we formulate a robust beamforming design that does not assume this knowledge, and propose a solution based on sample averaging. Numerical results demonstrate that the ISAC-based robust channel prediction and localization framework significantly improves prediction accuracy and link reliability compared with conventional schemes, such as communication-based localization and tracking and separated sensing and communication. These results highlight the potential of ISAC for reliable, adaptive, and high-frequency, blockage-prone wireless networks. Furthermore, the performance of the proposed framework is evaluated under severe blockage scenarios, which further demonstrates enhanced network reliability.

Memristor crossbars enable analog vector-matrix multiplication (VMM) which is promising for machine learning applications. Scaling matrix entries to lower memristor conductance levels reduces power consumption but increases the impact of memristor programming noise on VMM accuracy. To investigate how low-rank factorization can improve this trade-off, we extend the previously proposed factorized low-rank approximation scheme (FLAS) to support adjustable conductance scaling. We then derive closed-form MSE and power expressions for both FLAS and baseline VMM. Based on these expressions, we establish an analytical power-MSE trade-off framework to capture the coupled effects of approximation rank, replication allocation, and conductance scaling under constraints on memristor count and conductance scaling bounds. Numerical results demonstrate FLAS's power-MSE advantage across matrices with different singular value spectra. The power decomposition explains how TIA feedback resistance affect this advantage.

This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG). The model is trained on Copenhagen Center for Health Technology-Contextualized Arrhythmia Database (CACHET-CADB), which, to the best of our knowledge, is the first ambulatory ECG database with both physical and patient-reported contextual data. The model shows stable performance on different databases such as MIT-databases and the latest PyhsioNet/Cinc Challenge 2021 databases. Subsequently, the paper demonstrates how complicated ECG noise can be investigated by the SQA model and the physical contextual data.

The classification of hyperspectral images (HIs) still presents several challenges. One of them is the difficulty to obtain a large set of labeled samples to train the classifier. Semi-supervised learning methods have received much attention recently, as they require the initial labeling of a reduced number of image pixels and lead to very good results for practical application. One of the open problems in graph-based semi-supervised HI classification is the need to consider relative spatial relationship between pixels in the image to improve the smoothness of the solution. Kernel-based approaches using composite kernels have led to very good results, in which one kernel addresses the spectral properties of the pixels while a second kernel addresses some spatial properties. Most available solutions employ a spectral-spatial kernel which considers the spectral properties of a spatial region about each pixel. This work proposes a composite kernel approach that includes a third kernel dealing exclusively with the relative spatial position of the pixels. Experiments with real HI images show that the use of the new spatial kernel has led to improved classification results when compared to those previously reported in the literature. These results also shed some light on the relative contributions of the spectral and spectral-spatial kernels in semi-supervised HI classification.

Future radio access networks are expected to rely on large-aperture antenna arrays, for which an increasing portion of the coverage region may fall within the radiative near-field. In this regime, conventional far-field models become inaccurate, and the received spatial signature depends jointly on the propagation range and azimuth, making reliable uplink channel acquisition a key physical-layer challenge. Many existing near-field estimation works rely on simplified line-of-sight-dominant or single-path channel models, which fail to capture practical non-line-of-sight (NLoS) multipath environments. In contrast to prior single-path near-field partial relaxation (PR) formulations, in this paper, we develop a PR-based framework for near-field NLoS uplink channel estimation by modeling each user channel as a superposition of multiple spherical-wave components characterized by their ranges and azimuths. For the known-pilot case, we develop a greedy PR-based maximum likelihood estimator that iteratively extracts dominant propagation paths while mitigating multi-user interference. For the unknown-symbol case, we propose a PR-based rank-adaptive covariance-fitting approach that captures the multipath structure. We further derive the corresponding Cramér-Rao bounds for both cases. Numerical results show that the proposed multipath PR-based methods achieve strong estimation performance, remain close to the corresponding bounds, and outperform near-field two-dimensional multiple signal classification across the considered scenarios, supporting their relevance for future large-aperture uplink systems.

In genomics, imaging and clinical studies, only a few of many candidate predictors are often nonlinearly associated with a response that may be, e.g. binary, categorical, a count or a censored event time. The Terminating-Random Experiments (T-Rex) selector is a scalable variable selection method that controls the false discovery rate (FDR) by letting synthetic null variables (dummies) compete with the real predictors. While the FDR control theory embraces more general settings, to date, the T-Rex selector has been specified only for linear models. We propose a memory-efficient selection procedure with FDR control for generalized linear models and Cox regression by extending the recently developed virtual dummy construction to score-based forward selection for Bernoulli, Poisson, multinomial and Cox responses. The virtual-dummy-based selection path remains equal in distribution to explicit augmentation, so FDR control carries over under the same assumptions. Simulations confirm this equivalence and the power gained by correct model specification. Real-world applicability is illustrated on simulated genotypes and on cancer survival data.

Speaker distance estimators are trained almost exclusively on simulated room acoustics, because real recordings annotated with the true talker-to-microphone distance are scarce. We show that models trained this way transfer poorly. On three real corpora we evaluate, simply predicting the average distance of the corpus is more accurate than any learned model. Then, we ask how few labelled real utterances are needed to make a frozen, synthetic-trained estimator useful, and study post-hoc calibration maps that rescale its output without gradients or retraining. An analysis of the achievable error shows that what the calibration is not limited by the absolute accuracy of the estimator, but how well it orders utterances by distance, since a constant bias or a wrong output scale is removed exactly by the calibration itself. Balancing this against the cost of estimating each coefficient from few samples yields a criterion that accounts for which map wins on which corpus and at which annotation budget, together with a shrinkage variant that requires no hard decision. Our findings suggest selecting synthetic checkpoints by linear correlation with true distances rather than by absolute error. Code, datasets, and analysis are available at https://github.com/michaelneri/audio-distance-estimation.

The 3GPP 5G non-terrestrial networks (NTN) technology fundamentally relies on a global navigation satellite system (GNSS) fix at the user equipment (UE) to pre-compensate for user-link delay and Doppler shifts prior to any transmission, including the random-access procedure for initial access. In GNSS-denied or interfered environments, this dependency triggers preamble collisions and connection dropouts, undermining satellite-based global connectivity. To address this vulnerability, this paper introduces a novel single-satellite positioning framework designed for higher-frequency (Ku- or Ka-band) 5G NTN very small aperture terminals (VSATs) equipped with a phased-array antenna. The multi-step scheme derives an initial coarse position from angle of arrival (AoA) observables, extracts frequency of arrival (FoA) and time of arrival (ToA) measurements from low Earth orbit (LEO) downlink reference signals, and hybridizes them within a Levenberg-Marquardt navigation filter. Systematic evaluations across static grids and realistic dynamic urban trajectories detail the incremental gains of transitioning from single-epoch AoA error minimization to multi-epoch angular tracking, followed by the integration of Doppler and ranging observables. Grounded in a realistic error budget, the proposed framework reduces initial positioning uncertainty from tens of kilometres down to a few kilometres. This level of positioning accuracy, combined with the GNSS-resilient features introduced in 3GPP Release 20 NR NTN specifications, enables robust initial access in compromised operational environments with minimal relaxation of 3GPP standards.

We propose a bio-inspired approach that uses the inner-hair-cell (IHC) response of the Cascade of Asymmetric Resonators with Fast-Acting Compression (CARFAC) model to efficiently extract cyclic modulation from acoustic signals. We further investigate the contribution of IHC processing by comparing the CARFAC-IHC response with the CARFAC basilar-membrane (BM) filtering. Furthermore, the CARFAC-IHC and CARFAC-BM approach are benchmarked against conventional FFT Accumulation Method (FAM), Integrated Cyclic Modulation Coherence (ICMC), and Detection of Envelope Modulation On Noise (DEMON) approaches using the Case Western Reserve University (CWRU) bearing dataset and a real ShipsEar work-vessel recording dataset. The results demonstrate reliable recovery of characteristic cyclic components while substantially reducing the computational burden of conventional cyclostationary analysis.

The constant-Q transform (CQT) represents audio on a logarithmic frequency axis. Its nonstationary Gabor formulation is exactly invertible, but the unequal numbers of time coefficients in its bands complicate GPU computation. An exact factorisation combines spectral selection, conjugation, windowing, and reordering into a fixed map between one packed Fourier transform and the shorter band inverse transforms. The factors give waveform reconstruction, real adjoints for backpropagation, and bounds on arithmetic depth and block width; overlapping slices permit streaming with bounded memory. Tests on two GPU models show that Flash-CQT reduces analysis-synthesis round-trip time by factors of two to eight relative to a baseline computing the same CQT. The proposed implementation also uses over 30% less peak temporary workspace and reaches a negligible reconstruction error, with a signal-to-noise ratio of about 130 dB, in single-precision floating-point arithmetic. These advances make Flash-CQT a practical, computationally efficient front end for spectral analysis and modern audio machine-learning systems.

Industrial robots are rarely used for machining tasks due to their limited path accuracy. This accuracy is mainly limited by inaccuracies in the drive trains. Compliance and transmission errors occur in the joint gearboxes. While transmission errors have been extensively studied for strain wave gears, there is little research on these errors in cycloidal drives. This gearbox type is commonly used in industrial robots for medium to heavy payloads. It is proposed to model the mainly periodic transmission errors using a Fourier series where amplitude and phase are defined as a polynomial function of the main influence factors load-torque, velocity, and temperature. Measurements of the transmission errors were conducted using an experimental setup representing a single robot joint. In the evaluation of the measurement data, harmonic frequencies were related to mechanical properties of the cycloidal drive. These frequencies were used to identify the parameters of the polynomial Fourier series model. Compared to validation measurements, the derived model shows an average root mean square error of 0.026 mrad. It is proposed to use the output of the resulting model in a feedforward control approach to compensate the transmission errors and to increase the path accuracy of industrial robots.

Variational quantum algorithm is a leading approach for quantum chemistry and many-body physics on noisy intermediate-scale quantum devices. The performance depends strongly on the structure of the parameterized quantum circuits. Quantum architecture search (QAS) can automate ansatz design. However, it requires repeated training and evaluation of many candidate circuits, leading to high evaluation cost. In this work, we propose a noise-aware quantum architecture search framework based on ZX-calculus topological reuse (ZX-QAS). The framework encodes the search space with a ternary Gray-code mapping and integrates a noise-aware quantum neural network with a ZX-calculus topological reuse mechanism. The effectiveness of the framework is validated through ground-state energy estimation tasks and one-dimensional transverse-field Ising model tasks under noisy conditions. The results show that the ZX-QAS exhibits stable convergence and remarkably reduces the cost of expensive evaluations under noisy conditions.

Low-pass (LP) filtering is a fundamental operation in graph signal processing (GSP). Among finite-order nodal-domain methods, Lanczos-based filtering provides more accurate approximations of ideal LP filters than Chebyshev polynomial methods. We show that the approximation of Lanczos filtering can be further improved through algorithm unrolling and data-driven parameter learning. The key insight is that, because ideal LP filtering is a projection operation into the low-frequency eigen-subspace $\cS_K$, instead of approximating individual eigen-pairs of a graph Laplacian $\L$ as done in classical Lanczos, an unrolled Lanczos network can directly approximate $\cS_K$. Specifically, we first establish a theorem identifying properties of the Lanczos tridiagonal matrix $\T_m$ that promote accurate approximation of the low-frequency eigen-subspace $\cS_K$. Guided by this theory, we relax the orthogonality constraint on Lanczos vectors, resulting in Ritz vectors that better span $\cS_K$. To ensure numerical stability, we constrain $\T_m$ to be similar to a symmetric matrix, thereby guaranteeing real-valued eigenvalues. Experimental results on random graphs and learned graphs in two natural language processing (NLP) tasks show that our unrolled Lanczos network achieves superior ideal LP filter approximation compared to the classical Lanczos method.

Pilot-aided channel estimation is a decisive block in orthogonal frequency-division multiplexing (OFDM) receivers for both 5G New Radio (5G-NR) and Long-Term Evolution (LTE). A large body of estimators exists, from simple least-squares (LS) interpolation to statistically optimal linear minimum-mean-square-error (LMMSE) variants and, more recently, deep convolutional denoisers, yet no single estimator is uniformly best: the winner depends on the propagation scenario, the numerology, the operating signal-to-noise ratio (SNR), the mobility (Doppler), and the antenna configuration. In this paper, we quantify this fact through a unified study of eight literature estimators evaluated over the 3GPP TR~38.901 Urban-Macro (UMa), Urban-Micro (UMi), and Rural-Macro (RMa) channels generated with NVIDIA Sionna, for both 5G-NR and LTE numerologies, in single-input single-output (SISO) and $8\times2$ multiple-input multiple-output (MIMO) settings. We then propose a condition-adaptive multi-agent orchestrator that treats each estimator as an independent agent and dispatches, per operating condition, to the agent that is best on a validation split without any genie knowledge. The orchestrator tracks the per-realization oracle to within $1.07$~dB and improves the normalized mean-square error (NMSE) over the best fixed strategy by up to $3.6$~dB at high SNR, where the low-SNR champion is no longer optimal. Because the agents are independent, running them concurrently delivers this best-of-eight accuracy at essentially single-estimator latency: a data-parallel partition scales the wall-clock nearly as $1/K$ with $K$ workers (up to $6.9\times$), whereas naive by-algorithm partitioning is Amdahl-limited by the heaviest agent. The results substantiate multi-agent orchestration as a practical route to robust channel estimation across heterogeneous 5G-NR/LTE deployments.

Diffusion MRI microstructure mapping (MM) is conventionally solved voxel by voxel, ignoring the fact that tissue microstructure forms a spatially organized field. This isolation leaves each estimation problem ill-posed and nonconvex. We instead cast MM as a single global inverse problem, reconstructing the entire parameter field jointly from all measurements of a subject. An untrained neural representation supplies implicit spatial priors and eases the nonconvex optimization, requiring no training data, while coregistered T1-weighted anatomy contributes structural guidance that is freely available in standard protocols. On both synthetic and in-vivo data, our method compares favorably with established voxel-wise and learning-based baselines, suggesting global inversion is a promising alternative.

Frame refresh rate estimation is a fundamental step in identifying compromising harmonic frequencies in electromagnetic side-channel attacks. Methods based on discrete linear autocorrelation (DLA) are robust across different scenarios and require a computational complexity of $O(N\log N)$ for a signal containing $N$ samples. This work proposes reducing this complexity to $O(N)$ by exploiting prior knowledge of the target system's display resolution, as is available for certain models of the Brazilian electronic voting machine. Experiments using software-defined radios show that the proposed method preserves the accuracy of the conventional approach in the evaluated scenarios.

Concurrent long-range (LoRa) transmissions over the same time-frequency and spreading factor (SF) resources generally result in packet collisions, as the gateway cannot distinguish the overlapping signals from different end devices (EDs). This paper advocates a new fluid antenna multiple access (FAMA) framework for LoRa, referred to as lora-FAMA, to provide spatial opportunities for LoRa multiuser communications. In lora-FAMA, a gateway employs a single fluid antenna connected to only one radio-frequency (RF) chain, whose radiating element traverses the antenna aperture by sequentially visiting all candidate positions, i.e., fluid antenna `ports', within each symbol interval. The signal segments collected along the trajectory are compensated using the channel state information for the target ED. As a result, the desired signal is coherently accumulated, whereas the signals from other EDs experience unmatched channel variations and thus cannot be coherently combined. Applying the compensation separately to each active ED enables simultaneous multiuser transmission. We analyze the statistical performance of lora-FAMA under asynchronous transmissions and spatially correlated fading, as well as the large-aperture limiting case with independent and identically distributed fading. Numerical results show close agreement between the analytical and Monte Carlo results. It is revealed that, with a normalized aperture of $4\times4$ and $\mathrm{SF}=9$, a gateway can simultaneously serve more than $10$ EDs over the same frequency and SF resources while maintaining a symbol error rate below $10^{-4}$. These results demonstrate the potential of fluid antennas to enable LoRa multiple access without multiple RF chains.

We study the differential privacy (DP) of a graph shift operator (GSO) when an analyst observes the output of a graph filter. In particular, we study the setting in which the input signals to the graph filter are drawn from a differentially private distribution. Unlike approaches that perturb the GSO or the filter output, we use the randomness already present in the inputs to protect the GSO. This yields an equivalent level of privacy protection to that of the perturbation methods without adding noise, and thus a better privacy-utility trade-off. We provide an explicit characterization of the privacy loss and its certificate in terms of the zeros of the graph filter. In doing so, we show that the log-likelihood ratio between the releases of two adjacent topologies is governed by the distances from each zero to the graph frequencies of the two GSOs. Then, by uniformly bounding the log-likelihood ratio over the adjacent topologies, we obtain an explicit $(\varepsilon,\delta)$-DP guarantee for Gaussian inputs. We further show, via a Cramér–Rao bound, that the zero placement that limits the privacy loss also raises the floor on the adversary's reconstruction error. Finally, empirical validation is performed on a synthetic network of financial exposures, where the largest position a pair can conceal and the accuracy with which it can be sized are collinear across pairs. Both are set by the graph-frequency content of the pair, and the full network becomes recoverable only as the certified budget grows.

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

Near-field (N-F) line-of-sight (LoS) MIMO channels admit a compact representation through a small number of geometric parameters describing the relative transceiver geometry, enabling low-overhead channel acquisition without estimating the full channel matrix. This paper considers downlink (DL) channel acquisition under practical asymmetric user-equipment (UE) antenna capabilities with multiple DL receive antennas, but only a single active uplink (UL) transmit chain. In this setting, UL observations across the large BS aperture enable accurate estimation of the relative location of the UL-active UE antenna but are insufficient to determine the orientation of the entire UE array. We propose a two-stage geometry-based N-F LoS MIMO channel acquisition framework that distributes the estimation of the channel-defining geometric parameters between the BS and UE. First, a single UL pilot sequence enables the BS to estimate two BS-side reference angles that parameterize the relative location of the UL-active UE antenna. The BS conveys these estimates to the UE and transmits two DL pilot sequences, from which the UE estimates the remaining array-orientation parameter. We derive Cramer-Rao lower bounds (CRLBs) for the first-stage parameters and a first-order error-covariance approximation for the UE orientation that explicitly captures the propagation of first-stage estimation uncertainty. Numerical results characterize the resulting error propagation and demonstrate accurate estimation of the geometric parameters and reconstruction of the LoS MIMO channel using one UL and two DL pilot sequences.

This paper studies the effect of Reconfigurable Intelligent Surface (RIS) placement errors on optimal RIS deployment for Time-of-Flight (ToF)-based self-localization. We consider a setup in which a source transmits a signal, receives the RIS-reflected echo, and estimates its own position from the corresponding ToF measurements. Under the conditions of moderate, i.i.d. Gaussian RIS position errors and i.i.d. Gaussian ToF noise, we demonstrate that the placement uncertainty manifests as a geometry-independent scaling factor in the Cramér-Rao Bound (CRB). Consequently, we prove that the optimal RIS placement remains unchanged by the presence of RIS position errors. This result is established for A-, D-, and E-optimality criteria. Simulation results verify the analysis and show that optimized RIS deployments retain their advantage even in the presence of placement uncertainty.

Millimeter-wave (mmWave) to sub-terahertz (sub-THz) links can realize high data rates, yet are highly vulnerable to dynamic blockages due to high directivity, limited multipath, and dependence on line-of-sight (LoS) propagation. While structured and self-healing beams are promising solutions, it remains unclear how beam types perform under dynamic blockage. In this paper, we present an empirical analysis of structured near-field beams using two transmission policies. At one extreme, we study beam resilience without obstacle adaptation. At the other extreme, we empirically optimize beam parameters to study the limits of multiple beam types under idealized adaptation. We employ numerical analysis based on the angular spectrum method (ASM) and experimental validation using a sub-THz time-domain spectroscopy (TDS) platform to characterize beam performance over complete blockage events. For the scenarios considered, despite lacking self-healing, focused beams provide the strongest resilience when beam parameters are not adapted during a blockage event (i.e., when all beams are optimized only a priori). In contrast, when optimally adapted to obstacle position, curved beams provide the best performance. Lastly, although Bessel beams benefit from self-healing, this property alone can be insufficient to outperform optimized curved and focused beams. These findings provide guidance for blockage-aware beam selection and adaptation protocols.

We develop a curvature-domain formulation for continuous-aperture signaling, in which the transmit phase is represented through its second spatial derivative after quotienting out affine piston-and-tilt gauge freedom. The resulting gauge-fixed synthesis operator is bounded and compact, with sharp Poincare-Wirtinger constant $C_L=L^2/\beta_1^2$, and its modal Gram spectrum is available in closed form, $\rho_m=(L/\beta_m)^4$, with $\beta_m$ the roots of $\cos\beta\cosh\beta=1$. Under a bounded-support square-integrable propagation kernel the tangent operator is Hilbert-Schmidt, so the infinite-dimensional capacity is a well-defined Fredholm-determinant supremum. The optimal signaling law is a dual-budget generalized water-filling with one Lagrange multiplier for curvature power and one for phase excursion. From the exact nonlinear phase-only aperture law we derive the coherent tangent channel with an explicit Frechet remainder bound and a multi-chart atlas for large excursions. At the receiver, curvature inferred from noisy phase samples by second differences has a pentadiagonal noise covariance with spectral norm $\Theta(\Delta x^{-4})$. A deterministic diagnostic suite measures each mechanism against its closed form, with a null run beside every claim: the computed spectrum matches $(L/\beta_m)^4$ to relative error $3.78\times10^{-15}$; the tangent remainder has fitted slope 1.0000; the dual-budget law is solved with both multipliers strictly active to a KKT residual of $3.90\times10^{-15}$; the derivative-noise bound is approached to 0.999981; and the differentiation-limited branch is observed at exponent 0.2175, then collapses to 0.0576 once the mode count saturates at the Shannon number, while a flat-propagation null run holds at 0.2019. Curvature is thus a well-posed, gauge-invariant coordinate, and below the Shannon number the gauge, rather than the medium, governs the scaling.

Continuous-aperture and holographic MIMO systems motivate signaling that operates directly on large electromagnetic apertures rather than on a few antenna ports. This paper is the empirical companion to the operator-theoretic curvature-domain framework: a reproducible benchmark of curvature-domain signaling under a common scalar aperture-channel model, stress-testing the theory's dual-budget generalized water-filling law against the modal bases used in near-field and holographic MIMO. All methods share the same apertures, quadrature, Fresnel or Green-function propagation, phase-only constraints, power, phase-energy and curvature-energy budgets, receiver noise, phase quantization and training assumptions. The compared coordinates are curvature-regularized eigenmodes, raw phase coefficients, Fourier phase modes, polynomial and Zernike-like wavefront modes, near-field matched-focus profiles, random and optimized RIS phase codebooks, and SVD water-filling upper bounds. The claim is deliberately limited: curvature-domain signaling is a gauge-invariant, physically realizable coordinate system that can approach the phase-space SVD water-filling bound with fewer stable modes where derivative noise, phase quantization, sampling density or ill conditioning limit conventional bases. It is not a claim of new electromagnetic physics, nor that curvature modes dominate every baseline. We prove the SVD upper-bound relation for the discretized phase-control tangent space, derive pairwise-error and perturbation bounds, and report capacity, retained modes, symbol error, robustness, quantization, sampling, regularization, conditioning and cost, with 95% bootstrap confidence intervals on Monte Carlo results. A verdict table locates the regimes where curvature-domain signaling is engineering-relevant: moderate-to-high phase noise, coarse phase quantization, and non-Fourier-diagonal channels.