Papers

Power-domain non-orthogonal multiple access (NOMA) remains a widely studied technique for improving spectral efficiency and supporting dense connectivity in beyond-5G and 6G networks. Its main operating mechanisms, however, depend on the integrity of channel-state information (CSI). Power allocation, user ordering, pairing, clustering, and beamforming can all be distorted when the CSI consumed by the base station is deliberately biased rather than merely noisy. This article examines false CSI as an attack surface in power-domain NOMA. We organize the threat space using a compact taxonomy with two primary axes: magnitude, which distinguishes underreporting from overreporting, and ordering effect, which distinguishes order-preserving, boundary, and order-reversing attacks. We then show how coordinated false- CSI behavior, group-changing attacks, direction forgery, pilot spoofing, training-phase injection, and RIS-induced channel manipulation extend this basic taxonomy. Finally, we map each attack family to system-level impacts on power allocation, SIC reliability, scheduler behavior, fairness, throughput, and secrecy. The central message is that false CSI should be treated not only as a channel-estimation problem, but also as a control-input integrity problem for 6G NOMA.

We have developed enhanced models for large-scale channel parameters intended for terrestrial-to-non-terrestrial (NTN) communication in Urban environments for supporting devices above ground levels, such as drones. The models are formulated as functions of terminal height above ground and elevation angle, applicable to S-Band and Ka-Band frequencies. To ensure realistic models, an open-source 3D scene generator was utilized to create a diverse set of urban scenes across Europe. Sionna ray-tracing was employed to generate path-gain samples for outdoor terminals, covering a range of heights up to 300 meters and elevation angles up to 90{\deg}. Our proposed analytical functions show excellent agreement to ray-tracing data for line-of-sight (LOS) probability, shadow-fading (SF), and clutter-loss (CL). Furthermore, a comparative coupling gain analysis with existing 3GPP models reveals notable differences, particularly caused by smaller CL and higher LOS probabilities from proposed models. Designed for ease of use, these models can be seamlessly integrated into simulation tools, offering a practical solution for researchers and engineers.

Future low Earth orbit (LEO) constellations are expected to provide positioning, navigation, and timing (PNT) as a native service alongside broadband, removing the GNSS dependency inherited by 5G non-terrestrial networks. This is known in the literature as fused PNT. This paper asks how a multibeam satellites in a LEO constellation should share their beams and power between a communication service (COM) and PNT in the scenario when communications keep full priority. Each beam on a satellite can provide a communication service, a PNT service or both (this under certain limitations). Then, the PNT service is evaluated by the share of area the reach $95\%$-of-time availability for a fixed maximum power available on each satellite. This paper present this PNT availability question as a beam-power budget, as the PNT beams do not need to transmit at the same power as the communication beams thanks to the processing gain on the receiver. Two satellite beam scheduler policies are compared: one where on a cell the beam serving COM demand is exclusive for COM, therefore the PNT service in this cell is provided by the other satellites in line of sight, it requires no signal modification and attains $70.5\%$ / $88.2\%$ availability under homogeneous/population-weighted traffic profiles; and a second policy, the COM beam can provide also PNT signals, this in-beam ranging embeds the ranging signal in the COM waveform, reaching the ceiling at the cost of waveform redesign. The availability gap between the two policies is concentrated on the thin-overlap equatorial belt.

This letter addresses the challenge of cyclic prefix (CP) problem in orthogonal frequency division multiplexing (OFDM)-based on-off keying (OOK) generation for low-power Internet of Things (IoT) systems. We propose a CP-aware waveform design that generates the OOK signal over the entire CP-OFDM symbol duration, potentially breaking subcarrier orthogonality after passing through the channel if the CP is not matched. To enable such a mechanism, OOK has to be generated independently and a modification to the existing OFDM-based OOK generation block is needed. Additionally, a block-wise time shift can be applied to minimize CP mismatch. Analytical discussions and simulations confirm that the resulting interference on legacy subcarriers remains controlled, while OOK detection performance significantly improves, albeit with trade-offs that should be carefully considered in practical deployments.

Distributed radar sensing exploits spatial diversity to resolve occlusions and improve estimation accuracy. Realizing these gains, however, relies on the transmission of high-dimensional radar data to a Fusion Center (FC). This imposes significant demands on the wireless network, especially in dense, dynamic, and interference-prone environments like factories, where resilience and latency are critical. This paper studies the resource allocation problem in a capacity-constrained two-hop cloud radar network. We propose a buffered access protocol where sensors perform local spectral windowing to reduce data rates before transmitting measurements to the FC via intermediate Edge Servers (ESs). The resource allocation is formulated as a mixed-integer optimization problem aimed at minimizing the aggregate Cramer-Rao Lower Bound (CRLB) of the target parameters subject to fronthaul and backhaul capacity constraints. We develop an iterative solution algorithm based on Big-M formulation and Successive Convex Approximation (SCA). The proposed framework efficiently identifies the most informative sensor subsets and optimizes time-frequency resource assignments, ensuring high-fidelity sensing within stringent communication budgets.

Parameter estimation of periodic pulse trains is a critical task in numerous automated sensing and diagnostic applications. While estimation in the time domain provides superior accuracy in low signal-to-noise ratio environments, its high computational complexity frequently precludes its use in real-time systems. This paper investigates algorithmic optimizations to reduce runtime by leveraging recent advancements in computing architectures. Exploiting the inherent sparsity of the signal via sparse matrix multiplication kernels yields a substantial decrease in inference time. Furthermore, by separating the dense matrix projections into sequential cross-correlation and sparse summation steps, we fundamentally reduce both runtime and memory complexity. These optimizations drastically shrink the memory footprint, making time-domain estimation feasible for large datasets in real-time settings.

Small HAWT is a technology characterized by non-trivial critical points, basically because it is targeted for domestic use and therefore cheap manufacturing and control must conjugate with good efficiency under possibly complex flow conditions (especially in urban environment). Therefore, dynamical control optimization and noise and vibration mitigation are pressing issues for this kind of technology. Despite it is peculiar of small HAWTs that the generator constitutes a non-negligible fraction of the total mass and therefore the electromechanical coupling is relevant, condition monitoring of small HAWT generators is an overlooked topic. The present work is a test case study of damage diagnosis on a permanent magnet generator of a HAWT having 3 kW of maximum power and 2 meters of rotor diameter. The experimental analysis is conducted through wind tunnel tests and on a generator test rig where a damaged and an undamaged generators have been driven at different rotational speeds. Vibration measurements are collected in the wind tunnel through radial accelerometers near the rear bearing of the shaft and in the test rig through uni-axial accelerometers (fixed in radial positions, in order to be aligned with front and rear bearings). The test rig data result being particularly useful for studying the low-frequency tail of the vibration spectrum, where the characteristic frequencies of the bearing are located. The experimental data are analyzed in the time and frequency domain for feature extraction: a fault in the cage of the bearing supporting the generator is diagnosed using in particular the spectral coherence analysis.

This paper investigates the design of the on-off division multiple access (ODMA) transmission scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) systems with soft-output (SO) polar codes. First, a three-segment pilot-uncoupled coding scheme is introduced under the ODMA framework, which reduces the coding rate of the data segment without increasing the transmission overhead, improving the overall system performance. Building upon this architecture, a hierarchical pattern detection framework is developed. Specifically, a coarse-grained candidate set of transmission patterns is first identified through correlation operations. Based on this, a message-passing (MP)-based pattern detection algorithm is developed to iteratively estimate the posterior probabilities of transmission patterns, followed by the maximum a posteriori (MAP) estimation to obtain the precise pattern detection result. Furthermore, a joint pattern detection and data decoding algorithm based on the bit-wise SO information of polar decoder is investigated, where the posterior probability information provided by the polar decoder is exploited to refine the pattern detection and contribute to an improved accuracy. In addition, by leveraging bit-wise SO information of the successive cancellation list polar decoder, an MP-based iterative decoding algorithm is developed to significantly enhance the decoding performance. The proposed scheme simultaneously exploits the transmission gain of uncoupled-ODMA framework, the coding gain of polar codes in the short-blocklength regime, and the iterative decoding gain enabled by SO information, while the computational complexity is significantly reduced through the hierarchical detection framework. Simulation results demonstrate that the proposed scheme achieves strong robustness ...

Space-air-ground integrated networks (SAGINs) can provide ubiquitous and reliable connectivity for unmanned aerial vehicles (UAVs). However, air-to-ground links, which are typically dominated by line-of-sight (LoS) propagation, are vulnerable to passive eavesdropping due to the broadcast nature of wireless channels. To enhance physical-layer security, we investigate a SAGIN-enabled secure downlink communication system in which UAVs select service links among satellite, aerial, and terrestrial networks while adjusting the positions of the movable antenna (MA) array to fully exploit connectivity and spatial degrees of freedom for improved secrecy communication performance. Specifically, we maximize the secrecy energy efficiency (SEE) of a UAV swarm by jointly optimizing the MA positions, UAV trajectories, and link selections, subject to UAV mobility, MA movement, and link connectivity constraints. To reduce the real-time channel state information (CSI) acquisition overhead, we propose a channel knowledge map (CKM)-assisted multi-agent reinforcement learning framework. Specifically, the CKM is first constructed from sparse channel measurements via Kriging interpolation and is then leveraged together with satellite ephemeris information to enable efficient storage and retrieval of CSI. To reduce the action-space dimensionality and computational complexity, we model the MA array using rigid-body kinematics and adjust its position through global rigid-body translation, thereby constructing a low-dimensional hybrid action space for the joint optimization decisions. To align local decisions with system-wide performance under system constraints, we design an individual-team collaborative reward mechanism and introduce action masks to enforce constraints on UAV mobility, collision avoidance, MA regions, and connectivity capacity.

This paper studies multistatic measurement fusion when a target lies within the Tx–Rx (Transmitter-Receiver) baseline ambiguity zone, with particular emphasis on configurations involving two closely spaced stationary Tx–Rx pairs. Such configurations provide overlapping detectable regions and extend the effective detection range compared with sparsely spaced multistatic systems. However, in this region, the accuracy of range and bearing measurements degrades rapidly, and Doppler measurements often remain the only reliable information source. As a result, target trajectory estimation becomes highly challenging, with observability being marginal or even completely lost. To address this problem, the observability of target trajectories is analyzed under various conditions, enabling system designers to assess system performance in advance. A Doppler-only measurement fusion approach is then developed, employing a multiple-initial-point Maximum Likelihood (ML) nonlinear estimator for initial state estimation, followed by dynamic state updates using an Extended Kalman Filter (EKF). Simulation results are presented and shown to be consistent with the observability analysis.

This paper investigates secure downlink transmission assisted by a fluid active reconfigurable intelligent surface (FARIS), which enables both active reflection and dynamic port selection, offering enhanced flexibility for physical-layer security. We formulate a secrecy rate maximization problem that jointly optimizes the transmit beamformer, active reflection coefficients, and fluid port configuration under practical power constraints. To efficiently handle the resulting highly nonconvex problem, we develop a tailored alternating optimization (AO) framework that decomposes the original joint design into tractable subproblems, where each admits an efficient solution while preserving the system constraints, enabling an effective joint optimization of beamforming and FARIS reconfiguration. Numerical results demonstrate that the proposed FARIS-assisted design consistently outperforms the benchmarks. The results further highlight the robustness of FARIS against unfavorable eavesdropping geometries, confirming its potential as a powerful enabler for secure communications in challenging environments.

In this paper, we propose a robust transmission design for multi-user systems assisted by a fluid active reconfigurable intelligent surface (FARIS), which enables both active reflection and dynamic port selection and thereby offers enhanced flexibility, under imperfect channel state information (CSI). We formulate a robust minimum sum-rate maximization problem by jointly optimizing the base station beamformer, the utilized FARIS coefficients, and the active element selection, while explicitly accounting for CSI errors and practical power constraints. The resulting problem is inherently nonconvex due to the coupled optimization variables and discrete port-selection structure. To tackle this challenge, we first reformulate the original problem via a weighted minimum mean square error (WMMSE) approach and then devise an alternating optimization (AO) framework, where each resulting subproblem admits efficient solutions and the overall algorithm converges to a stationary point. Simulation results demonstrate that the proposed robust FARIS scheme consistently outperforms conventional designs, highlighting the effectiveness of jointly leveraging degree-of-freedom (DoF) enhancement and active signal amplification under CSI uncertainty.

Multi-Threshold (MT) sampling fixes voltage levels and records when a waveform crosses them. The resulting signal-generated event stream is governed not by a prescribed clock but by the nonuniform geometry of the realized crossing set. Within classical nonuniform sampling, we develop a retained-event MT theory for static, uniformly spaced thresholds. For a realized retained MT time set, Beurling's weak-limit theorem gives the exact necessary-and-sufficient condition for stable sampling of $PW_\Omega$. For a fixed signal class, the same principle gives the a priori criterion: uniform stable MT sampling is equivalent to Bernstein uniqueness for every set in the class weak-limit hull. This criterion reduces to computable forms under structured priors. Periodic templates give finite fiber/rank tests, periodic-gap streams give a Shannon-type complete-interpolation boundary and a stable-sampling density rule, and finite-state gap priors reduce noncritical certification to a maximum-cycle-mean condition. Kadec-type local certificates, retained-density bounds, and finite-matrix tests provide practical sufficient conditions for finite active records, while conditional perturbation bounds quantify how jitter, front-end noise, and threshold error consume a clean sampling margin under event correspondence. The theory also identifies the unavoidable boundary of this model: unrestricted finite-energy retained MT records have zero full-line lower Beurling density and therefore cannot provide a universal prior-free sampling theorem for $PW_\Omega$. Within retained-event point sampling, the MT recovery question is resolved precisely: exact recovery is governed by event-set geometry, computable guarantees require structural priors or finite-dimensional/tail assumptions, and the unrestricted static finite-energy problem is impossible.

Stress detection using physiological signals has gained significant attention due to its impact on both physical and mental health. While existing approaches based on machine learning and deep learning achieve strong predictive performance, they often rely on black-box models and fail to capture individual variability in physiological responses. In this work, we propose an interpretable and personalized framework for stress detection using electrocardiogram (ECG) signals based on motif discovery and Matrix Profile analysis. Instead of training a classifier, the method learns subject-specific baseline cardiac behavior by extracting recurring heartbeat patterns (motifs) from ECG signals. Stress is then detected as a deviation from these baseline patterns using a distance-based anomaly score. Experiments are conducted on the WESAD dataset using a carefully designed train-validation-test protocol to ensure reliable evaluation. The results show that the proposed approach can effectively detect stress for several subjects while providing clear interpretability through direct comparison of ECG patterns. However, the performance varies across individuals due to differences in physiological responses, with some subjects exhibiting minimal morphological changes under stress. Additional analysis incorporating heart rate variability (HRV) features reveals that while HRV can improve performance in certain cases, its contribution is not consistent across all subjects. These findings highlight the importance of personalization and interpretability in physiological stress detection and demonstrate that motif-based approaches provide a meaningful alternative to black-box models, while also revealing inherent limitations due to inter-subject variability.

This paper presents two interpretable machine-learning frameworks for quality screening of e-transaxle assemblies in electric vehicles: a Stagewise Wavelet Isolation Forest (SWIF) framework and a short-time Fourier transform (STFT)-based diagnostic framework. High-dimensional vibration signals acquired from front and back accelerometers are analyzed across multiple operating stages to capture stage-dependent vibration behavior. In the SWIF framework, signals are decomposed using a five-level Daubechies-4 discrete wavelet transform, and blockwise mean-squared coefficients are extracted from the selected wavelet detail level to obtain compact multiscale features. In the STFT-based framework, dominant-frequency trends are extracted from time-frequency representations and summarized through regression coefficients with respect to instantaneous motor speed. Anomaly detection models are trained using accepted production units under the assumption that only a small fraction of accepted assemblies contain latent defects, and their performance is evaluated using road-tested units with validated quality outcomes. Experiments on production and road-tested e-transaxle units show that both approaches provide interpretable diagnostic information, while SWIF achieves the most favorable balance between defect detection and false-positive control. Compared with the STFT-based method and alternative anomaly detectors, SWIF combined with Isolation Forest yields lower anomaly rates within the Accept population while identifying high-risk units from the Reject population. The stagewise structure further localizes anomalous behavior to specific operating conditions, supporting root-cause analysis and targeted process improvement.

Tri-hybrid multiple-input multiple-output architectures have recently emerged as a promising enabler for next-generation wireless systems, as they potentially provide enhanced design flexibility without a proportional increase in hardware cost or power consumption. However, the resulting triple-domain coupling renders beamforming optimization challenging and computationally demanding. This paper develops a fast tri-hybrid beamforming framework for multiuser downlink systems employing dynamic metasurface antennas (DMAs). Based on the equivalence between weighted sum-rate maximization and weighted sum-minimum mean square error minimization, an iterative algorithm is first derived under per-DMA input power constraints, with all update equations available in closed form, but convergence inherently remains slow. To enable real-time operation, the algorithm is further unfolded into a trainable finite-iteration architecture using graph neural networks that ensure permutation equivariance and support varying numbers of users. Trained on ray-tracing channel data, the unfolded method achieves comparable or higher system sum-rates while reducing runtime by more than an order of magnitude. The method also demonstrates strong scalability, robustness, and generalization across various environments.

Integrated sensing and communication (ISAC) is regarded as a key technology for sixth-generation wireless networks, allowing sensing and communication operations to jointly utilize the same spectrum and hardware infrastructure. However, in practical uplink ISAC systems, timing offset (TO) and carrier-frequency offset (CFO) introduce phase distortions across subcarriers and OFDM symbols, which can severely degrade both data detection and sensing-parameter estimation. In this paper, we propose a data-aided variational Bayesian (VB) framework for asynchronous uplink OFDM-ISAC systems. Specifically, the received signal is modeled as a sparse multipath superposition, where the transmitted data symbols, complex path gains, spatial frequencies, delay-Doppler parameters, and synchronization parameters are jointly inferred. To enable tractable inference, we develop a mean-field VB algorithm in which von Mises distributions are used for gridless updates of the angular and delay-Doppler phase parameters, while a Gamma-Gaussian prior is adopted to promote path sparsity. A key feature of the proposed framework is its data-aided sensing capability: after initial pilot-based estimation, the detected data symbols are exploited as additional observations to refine the channel and sensing parameters. This substantially increases the effective sensing resources without requiring extra pilot overhead. The simulation results demonstrate that the proposed approach achieves superior performance compared with SAGE, SBL, AB2FM, and pilot-only VB baselines in terms of symbol error rate, channel reconstruction accuracy, path-parameter estimation, TO/CFO estimation, and 3D localization accuracy. The results also demonstrate that ignoring TO and CFO leads to severe sensing degradation, highlighting the importance of synchronization-aware and data-aided receiver design for ISAC systems.

Low-Earth-orbit (LEO) constellations offer dense signals of opportunity (SOP) for positioning, navigation, and timing (PNT) without dedicated navigation infrastructure. However, using these signals passively, by only listening to the downlink, is difficult when beacon/pilot components are weak, signals from multiple satellites overlap, or the receiver cannot reliably identify which satellite generated a given observable. This paper proposes a low-power direct-sequence spread-spectrum (DSSS) watermarking framework that embeds a recoverable pseudonymous satellite identifier and keyed authentication field into a LEO downlink signal. The watermark carries a public satellite pseudonym for identification and a keyed HMAC-SHA256-based authentication field for authorized verification, enabling passive detection and verification without full downlink demodulation. We evaluate the framework in a multi-satellite simulation using Starlink two-line-element (TLE)-derived geometry, Doppler, and link-budget scaling with Sionna RT-based local propagation. The results demonstrate candidate-satellite detection, public-ID recovery, and keyed verification under co-observed satellite interference, with limited perturbation to the simulated beacon/pilot and primary downlink components. The framework targets future transmitter-enabled LEO systems while remaining transparent to legacy receivers that do not process the low-power watermark.

Oscillator synchronization is a widely used sound-synthesis technique, but straightforward digital implementations suffer from aliasing artifacts. This paper presents an alias-free method for digital emulation of oscillator synchronization of arbitrary periodic waveforms based on additive synthesis. Starting from a finite set of Fourier-series coefficients representing a bandlimited free-running waveform, we derive linear spectral-resampling transforms that map these coefficients to those of the bandlimited synchronized waveform. Beyond conventional hard synchronization, the proposed approach also supports two additional soft-synchronization modes. To address the high computational complexity of the proposed method, we introduce HASY, a 6 mm^2 application-specific integrated circuit (ASIC) fabricated in 65 nm CMOS technology. HASY generates one 96 kHz, 24 bit alias-free synchronized waveform with up to 512 harmonics and computes the spectral-resampling transform within only five audio-sample periods.

Cell-free massive multiple-input multiple-output (CF-mMIMO) promises uniform service across the coverage area, and this service relies on accurate channel estimation for every user equipment (UE). These estimates are obtained from orthogonal pilot sequences, whose number is bounded by the channel coherence block. As a result, the sequences are reused as the UE density increases and the resulting pilot contamination limits the scalability. This paper proposes a joint user association (UA) and pilot assignment (PA) framework built on the adaptive phase-shifted (APS) pilot design, in which all UEs share a single full-band pilot and a per-UE phase shift applied in the frequency domain places their channel impulse responses (CIRs) in contiguous, non-overlapping intervals of the time domain at the access point (AP). The number of UEs that an AP can separate is thus set by the number of subcarriers rather than by the number of orthogonal sequences, and the UA is formulated as a $\mathbf{0/1}$ knapsack problem whose item weights are the numbers of taps and whose capacity is the number of subcarriers. Analysis and simulations show that the proposed design more than doubles the number of UEs scheduled by the conventional interleaved (CI) benchmark. It attains the same mean-squared error (MSE) as the CI benchmark under an equal total pilot power, and a lower MSE under an equal per-subcarrier power. It further improves the 5th-percentile connectivity and Jain's fairness index by penalizing the number of existing connections of a UE in the value assigned to each candidate link.

In electric guitars, the vibrations of the strings are typically sensed by coils of wire combined with a magnet, called pickups. The pickups and their position along the strings contribute strongly to the instrument's sound. Most guitars feature one to three pickups, each spanning across all strings with fixed positions and generating a single mono output. The work of this Master's Thesis at ETH Zürich introduces a new pickup system called PolyMap, which senses each string individually and at multiple locations. The system is demonstrated with a custom-made eight-string guitar that contains eight pickups per string for a total of 64 pickups. The signals from these 64 pickups are individually digitized inside the guitar and transmitted over a multichannel audio digital interface (MADI), a low-latency digital audio interface, to a computer for further processing. PolyMap enables high-resolution sensing of an electric guitar's strings and enables extensive post-processing capabilities for musicians, audio engineers, and researchers. To the best of our knowledge, this is the first polyphonic guitar pickup system with such a complete feature set.

Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether other physiological properties of source contact photoplethysmography (PPG) remain preserved recording by recording. We evaluated property-specific PPG-to-rPPG recoverability on 655 recordings from the Multi-Domain Mobile Video Physiology Dataset using CHROM as a fixed camera-rPPG observation pathway. The pathway reproduced the published CHROM correlation regime, with heart-rate MAE of 15.26 bpm and Pearson correlation of 0.0801. Matched-versus-shuffled validation revealed modest recording-specific autocorrelation correspondence, while spectral and recurrence-rate measures showed little matched discrimination. Maximal Lyapunov exponents showed essentially no recording-specific PPG-to-rPPG correspondence, with correlation of 0.0231 and permutation p-value of 0.5584, despite population-level overlap. Endpoint discrepancy exhibited Fitzpatrick-associated heterogeneity after adjustment for lighting and motion, including a Fitzpatrick VI versus III contrast of 9.32 bpm, while dynamical discrepancy showed no corresponding gradient. Aggregate RGB signal-to-noise ratio did not materially account for the endpoint contrast. In subject-held-out analysis, adding motion and lighting consistently reduced MAE across linear, ridge, and random-forest learners relative to rPPG-HR-only calibration, with reductions up to 13.32 percent. These findings show that recoverability is property-specific: physiological properties differ in recording-specific preservation and dependence on observation conditions, and population-level plausibility does not establish preservation of individual recordings.

Range-Doppler (rD) maps produced by chirp- sequence (CS) radar systems are fundamentally limited in reso- lution by bandwidth, carrier frequency, and coherent processing interval constraints. Improving resolution through hardware is often impractical due to regulatory, cost, and real-time operation requirements. In this work, we investigate deep learning-based super- resolution of rD maps in both range and Doppler using a real- world dataset collected with an Infineon millimeter-wave CS radar. We propose a memory-efficient transformer architecture based on dual-path shifted window (DPSWIN) attention, which combines axial/dual-path attention with 1D shifted window (SWIN) attention for scalable processing of high-dimensional radar data. Our model is benchmarked against existing 2D SWIN attention-based super-resolution models, which we adapt to the rD-map super-resolution task. We prioritize reconstruction fidelity over perceptual quality and employ root mean squared error (RMSE)-based training objectives. We avoid adversarial or perceptual losses that may introduce visually plausible but physically incorrect structures. In addition, we incorporate CFAR-based target-detection losses to optimize downstream target detectability in the super-resolved rD maps. We further study the impact of signal processing and training design choices on the super-resolution, including magnitude compression, spatial upsampling strategies, and loss formulations. Experimental results demonstrate that the proposed framework achieves computationally efficient rD map super-resolution on previously unseen real-world environments.

Data-driven signal decomposition methods decompose a signal into its underlying components in a flexible and adaptive way, taking into account the signal characteristics. Here we focus on univariate signals, whose decomposition is ill-posed. Classical univariate approaches – like variational mode decomposition – constrain the solution space (e.g. through narrowband priors), and often require the number of components to be known in advance. These assumption, however, limit the algorithm's usage in certain real-life applications. Instead, we exploit the flexibility of neural networks to replace fixed (narrowband) priors with data-driven priors. Our model, called Iterative Deep-Learning-Based Signal Decomposition (IDSD), iteratively extracts an adaptive number of various types of components from a signal, with no restrictions on the bandwidth of a component. We show superior performance of IDSD both in a controlled setup with synthetic data, and on two real datasets concerning tidal waves and physiological measurements.

Industrial Internet of Things (IIoT) networks widely adopt Ethernet technologies, such as 100BASE-TX, for industrial communications. As industrial networks continue to scale, reliable device authentication becomes increasingly important for preventing device impersonation and unauthorized access. Physical-layer fingerprinting (PLF) exploits device-dependent fingerprint features in transmitted signals and provides a hardware-based approach for terminal authentication. However, the distinguishable space supported by 100BASE-TX physical-layer fingerprints and its capacity boundary remain largely unexplored. To analyze the capacity of physical-layer fingerprints, this paper proposes a nonlinear and impulse-response model (NAIM) that characterizes device-dependent waveform differences in 100BASE-TX transmitted waveforms. The nonlinear component captures steady-state level deviations, while the impulse-response component describes the transition response during level transitions. The 100BASE-TX transmitter waveform requirements, the observation resolution determined by noise and analog-to-digital conversion (ADC) quantization, and the target bit-error ratio (BER) constrain the admissible fingerprint space. Under the NAIM model, the fingerprint-space capacity of 100BASE-TX terminals is derived as approximately $2.96\times10^{10}$ distinguishable states. Experiments on signals collected from 48 NICs under two cable conditions estimate a Gaussian-equivalent empirical capacity from the measured inter-device and within-device variations. Under the 5-m cable condition, empirical capacity and closed-set identification consistently rank the three NIC models, and a larger empirical capacity yields higher identification accuracy. These results demonstrate that the proposed capacity analysis provides a pre-deployment assessment for physical-layer fingerprinting in IIoT.

The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary classification. To reduce hardware complexity, the receiver is equipped with cascaded metasurfaces terminating in a single radio-frequency chain. A front metasurface layer applies a fixed nonlinear response to the incoming signal, acting as the ELM's activation function. Subsequent tunable linear metasurface layers physically approximate the trained network weights directly in the wave domain. Numerical evaluations across diverse datasets showcase that our XL MIMO architecture achieves classification accuracy comparable to idealized digital models, thereby proving the viability of low-complexity, wave-domain OTA learning.

Emerging integrated sensing and communication (ISAC) systems based on distributed MIMO (D-MIMO) enable radio imaging by exploiting spatial diversity across multiple access points (APs). However, joint sensing and communication introduce mutual interference between communication and sensing signals. In this paper, we propose a downlink D-MIMO ISAC framework that allocates orthogonal subcarriers to sensing and communication to eliminate inter-function interference. We consider a phase-coherent architecture in which single-antenna APs serve communication user equipment (UEs) while constructing a reflectivity image of the environment. We develop a two-timescale resource allocation framework that minimizes reconstructed-image entropy subject to a communication spectral-efficiency (SE) constraint. The proposed design follows a communication-centric policy, where imaging uses the resources left after satisfying the communication requirement. The long-timescale optimization (LTO) determines AP modes and subcarrier assignment, including the partitioning between communication and sensing and the allocation of sensing subcarriers among transmit APs, using synthetic scenarios with random UE and target distributions. The short-timescale optimization (STO) adapts the communication-sensing power-splitting factor to preserve the SE constraint in the current scenario. Numerical results show that the proposed orthogonal subcarrier allocation achieves a superior sensing-communication trade-off compared with conventional superposition, where sensing and communication share the same subcarriers. Finally, we evaluate coherent and non-coherent imaging receivers and identify the synchronization regimes in which each approach provides better imaging and localization performance.

Islamic prayer is a structured movement activity that offers a distinctive setting for behavioral biometrics: all participants execute the same action sequence, so identity must be inferred from differences in execution. We collected inertial data from 95 participants using their own smartphones during nightly congregational Islamic prayer (Taraweeh). A label-aware pipeline converts long recordings into structurally complete two-rakaah behavioral samples (prayer units). This allows us to design unit-level and complete-session behavioral biometrics protocols. To address arbitrary smartphone orientation in worshippers' pockets, we study two motion representations: rotation-invariant magnitudes with gravity-relative acceleration components, and a metadata-aware Qibla-referenced canonicalization that harmonizes platform conventions, reconstructs device-to-world attitude, corrects Android magnetic north to true north via WMM2025, and expresses acceleration and angular velocity in a common Qibla-left-up frame. Under unit-level protocol, the invariant and Qibla-referenced representations reach learned pairwise Random Forest AUC/EER of 0.9932/4.07% and 0.9923/3.96%; under complete-session holdout, 0.9700/7.62% and 0.9618/7.81%. Qibla-frame directional ablations show the complete six-axis representation is strongest overall, with acceleration retaining most learned-verification performance and vertical motion the strongest single-axis cue. A Qibla-referenced SimCLR experiment further yields participant-template AUC 0.9805-0.9817 and EER 5.81-6.24% across two unit-level runs. Signal-, descriptor-, prayer-component ablation analyses show participant identity is distributed across movement dynamics rather than concentrated in one signal or posture. These results establish structured prayer movement as a measurable cross-session behavioral biometric.

This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regularized multitask frameworks typically assume a known structure, practical settings often require learning inter-task dependencies directly from distributed data. We propose a decentralized two-phase strategy that first estimates a generalized graph Laplacian from noisy non-cooperative stochastic gradient iterates, and subsequently exploits the learned graph to enable cooperative multitask diffusion learning. This framework is motivated by a Gaussian Markov random field prior, which gives rise to a decentralized maximum likelihood estimator for the graph Laplacian. The analysis quantifies the Laplacian estimation error and its propagation to the steady-state performance of the multitask diffusion recursion, and introduces a topology sensitivity index to capture the effect of network heterogeneity. Simulation results corroborate the theoretical findings and demonstrate that cooperation enabled by the learned task graph significantly improves performance over non-cooperative learning, while approaching the true-graph baseline when the estimation stepsize is sufficiently small.

This paper presents autowerkstatt4null, a three-year initiative to empower independent automotive workshops with AI-driven, federated diagnostics. The project was funded by the German Federal Ministry for Economic Affairs and Climate Action. The project enhanced the initial diagnostic workflow through newly developed technologies and architectural concepts, incorporating real user feedback. The resulting demonstrator showcases key outcomes: a modular measurement platform enabling flexible hardware integration, a secure data exchange hub for trusted collaboration, asynchronous online diagnostics that decouples analysis from workshop constraints, and a digital academy supporting technician upskilling. Together, these innovations demonstrate how independent workshops can access advanced diagnostic capabilities previously limited to manufacturer tools, strengthening competitiveness and sustainability in the sector. The project repository is available at: https://github.com/nabla-B/paper_aw4null-overview.