Papers

In this paper, we propose a signal processing concept, signal processing in networks (SPIN), that executes signal processing tasks directly on programmable switches. Traditionally, signal processing tasks, such as denoising, decomposition, and dimensionality reduction, are performed on edge or cloud servers, i.e., raw streams are transmitted from sensors. This imposes a heavy processing load on edge/cloud servers and requires substantial bandwidth. SPIN addresses this problem by performing signal processing on the forwarding path. As an initial realization of SPIN, we implement a DCT-based block transform on a programmable switch. We redesign its computation using a lifting-based lattice structure, multiplierless approximation, and pipeline partitioning across stages in a programmable switch. We verify at the compiler level that the proposed implementation satisfies the strict hardware constraints of the Intel Tofino2–specifically regarding pipeline stages, packet header vector, and ternary content-addressable memory usage–and therefore enables line-rate processing. We further quantify the per-packet latency of the ingress match-action pipeline. The SPIN-oriented preprocessing adds 46 cycles (approximately 31 ns at 1.5 GHz). The total ingress match-action unit latency is approximately 87 ns, which remains consistent with the low-latency operation targeted by SPIN. Furthermore, we implement the proposed block transform on a BMv2 switch using P4 and evaluate it as preprocessing for in-network machine learning. Experimental results demonstrate that the proposed method improves classification performance over unfiltered baselines.

This paper considers channel state information (CSI) prediction using neural networks for time-varying multi-antenna channels from noisy historical observations. The main challenge is to exploit temporal correlation while distinguishing channel evolution from observation noise. We propose JEPA-kMMVAE, which combines a joint-embedding predictive architecture with a k-memory Markov variational autoencoder to predict future CSI through temporally structured latent representations. The framework supports direct prediction and residual corrections relative to the latest latent state and noisy CSI observation. Simulation results show that the residual configuration outperforms the evaluated baselines at moderate and high input CSI signal-to-noise ratios, while sequence baselines remain more accurate under stronger noise.

Time-varying aggregate interference from low-Earth-orbit (LEO) satellites challenges terrestrial-network (TN) protection, whereas conservative control degrades LEO quality of service (QoS). This letter proposes a hierarchical framework combining geometry-based interference estimation, QoS-constrained power control, adaptive energy detection, large language model (LLM)-assisted candidate ranking, stay-or-switch decisions, and target-band selection, with deterministic verification. The current band is retained only when both spectrum-hole and power-feasibility conditions hold, with periodic reevaluation. Over a 300-s horizon with 0.5-s sampling, the proposed method keeps the aggregate INR from exceeding the $-12.2$~dB threshold and achieves a time-averaged mean LEO-link rate of $320.97$~Mbps with $100\%$ QoS satisfaction. It markedly outperforms power-only and satellite-muting in jointly protecting the TN and preserving LEO QoS.

Generative MRI reconstruction maps an initial source distribution to the target image distribution along a learned recovery trajectory. Common generative methods, such as diffusion and flow, typically learn fine-grained local dynamics whose integration defines this global mapping, hence requiring many network evaluations at inference. Existing few-step sampling approaches can lower computational burden by approximating large transformations from local dynamics, albeit at the expense of reconstruction fidelity. FLEX-MRI instead partitions recovery into a small number of finite intervals between retained states defined by progressive changes in k-space support, and directly learns the reconstruction change across each interval. Because such finite transitions can admit multiple plausible outcomes, FLEX-MRI combines support-relaxed finite-transition learning with stochastic exploration. During training, randomized k-space sampling masks define finite-transition targets with varying support, while the prediction loss remains unrestricted by each individual mask. At inference, FLEX-MRI perturbs a random subset of currently estimated k-space coefficients, allowing the learned predictor to stochastically explore alternative transition paths. Each predicted state is then reconciled with the acquired measurements through data consistency. We theoretically show that this stochastic exploration induces nonzero transition-level variation through the learned recovery map and data consistency without requiring an auxiliary generative sampler. Experiments on multi-coil MRI datasets show that FLEX-MRI outperforms competing methods while maintaining computational efficiency comparable to few-step baselines.

We study the SNR, aperture, and port count needed to meet a target outage in a single-user downlink with a fixed transmit antenna and a single-RF fluid antenna receiver. For known spatially correlated Rayleigh channels and ideal port selection, a sampling-free residual certificate controls reduced-rank outage error over a continuous SNR interval. Positive Bessel features support large arrays, and bounded importance sampling converts the certificate into simultaneous design intervals. At 1 bit/s/Hz and outage $10^{-5}$, a four-wavelength, ten-port receiver requires 4.40–4.55 dB. Reducing its port count from 256 to ten costs 0.70–1.25 dB under a joint 95% probability statement. At 3.5 wavelengths, ten ports require 4.90–5.05 dB. Integer-port screening across 3–8 dB shows how aperture and port requirements change with available SNR; an effective-SNR transformation extends these decisions to other rates. The reported intervals include model and sampling uncertainty under checked Gaussian covariance inputs. A constructive slow-growth rank bound and a 16–1024-port study support compact evaluation as port count grows.

Consumer rotating devices, including washing machines, air conditioners, electric fans, robotic vacuum cleaners, and home fitness equipment, increasingly rely on vibration sensing for intelligent condition monitoring and predictive maintenance. Ensuring reliable operation in these resource-constrained appliances requires efficient, lightweight artificial intelligence (AI) solutions capable of processing non-stationary signals under real-world operational interference. This paper proposes a lightweight AI-assisted vibration sensing framework that employs continuous wavelet transform (CWT) and Hilbert-Huang transform (HHT) to convert one-dimensional (1-D) vibration time-series signals into two-dimensional (2-D) time-frequency representations. These representations are subsequently analyzed using lightweight deep learning architectures, specifically MobileNetV3-Small, ConvNeXt-Tiny, and the You Only Look Once version 11 classification model (YOLOv11-cls). Public bearing benchmark datasets, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance Systems (IMS), are utilized as representative rotating components to investigate model generalization. The proposed framework is designed with eventual embedded deployment in consumer electronics in mind. Experimental results demonstrate that CWT combined with YOLOv11-cls achieves high diagnostic accuracy, while MobileNetV3-Small provides an attractive low-latency alternative for computationally constrained edge platforms. Conventional random splitting is also compared with dataset-specific structured isolation. The results highlight the importance of avoiding data leakage and quantify tradeoffs in classification accuracy, parameter count, floating-point operations (FLOPs), model footprint, preprocessing cost, and central processing unit (CPU) inference latency.

Moving dilated arrays (MDAs) can increase the uniform degrees of freedom (uDOFs) while alleviating mutual coupling, but their performance is susceptible to unknown gain and phase errors (GPEs). To deal with this problem, we investigate self-calibration direction-of-arrival (DOA) estimation for MDAs with unknown GPEs. First, multi-position observations are phase aligned to construct an equivalent virtual array with an enlarged aperture. Next, the motion-induced relationships among the GPEs are exploited to establish explicit calibration constraints. By leveraging the orthogonality between the steering vectors and the noise subspace, the joint estimation of the DOAs and GPEs is formulated as a constrained optimization problem. To solve this problem, a two-stage alternating optimization (AO) algorithm is developed. In the first stage, the GPEs are estimated in closed form with the DOAs fixed. In the second stage, the estimated GPEs are used to calibrate the covariance matrix, after which the DOAs are refined using the multiple signal classification (MUSIC) algorithm. To obtain reliable initial estimates, a rotational invariance-like (RIL) initialization method is further developed by exploiting two translated virtual subarrays with identical GPEs. Furthermore, the Cramer-Rao bound (CRB) is derived to characterize the theoretical performance limit of the considered system. Simulation results demonstrate that the proposed method converges reliably and achieves accurate DOA estimation under different array apertures, numbers of snapshots, GPE levels, and numbers of sources.

The shock response spectrum (SRS) characterizes the peak responses of single-degree-of-freedom systems to transient acceleration. Because the mapping from an acceleration time series (waveform) to its SRS is many-to-one, inverse synthesis seeks candidate waveforms consistent with a prescribed spectrum. We extend an earlier conditional variational autoencoder (CVAE) framework with a recurrent decoder that predicts multiscale wavelet coefficients and reconstructs waveforms through an inverse discrete wavelet transform. Individual models exhibit limited variability under latent sampling, motivating an ensemble of independently trained models spanning different mother wavelets and decomposition levels. Each member produces a separate candidate waveform for the same target SRS. Evaluation on four held-out benchmark datasets shows lower mean and median spectral errors for the illustrated wavelet model than for the earlier direct time-domain CVAE and a sum-of-decayed-sinusoids (SDS) baseline. A conditioning study examines frequency-grid and encoding choices. Comparisons with published inverse-SRS methods demonstrate superior performance across four holdout benchmark datasets (two operational shock datasets, one earthquake dataset, and one synthetic shock dataset) and competitive performance on synthetic targets reported by Brake. Single-model inference requires approximately 0.1-0.6 ms per waveform during batched inference compared with 5-8 s for SDS, excluding offline training. This low inference cost enables rapid generation of distinct SRS-compatible candidate waveforms across the ensemble, although their physical suitability requires assessment beyond spectral agreement.

Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has achieved notable success with static sound sources, real-world scenarios often involve moving targets, giving rise to the more challenging problem of Dynamic Audio-Visual Navigation (D-AVN). Existing approaches largely rely on semantic auditory features, which limits generalization to unheard sound categories and hinders fine-grained spatial reasoning. To address these challenges, we propose a novel Two-Stage Multi-modal Learning framework for D-AVN. In the Physical Alignment stage, the proposed Intensity-Calibrated Geometry Encoder (ICGE) performs physics-informed alignment of sound intensity with visual geometry to capture category-agnostic spatial correspondence. In the Scene Aggregation stage, a Transformer-based Cross-Modal Scene Aggregator (CMSA) dynamically integrates multi-modal features to construct a robust state representation for policy learning. Extensive experiments on Replica and Matterport3D demonstrate that our method achieves state-of-the-art success rates, while significantly improving generalization and navigation efficiency in complex environments and under unheard sound scenarios.

Time-modulated intelligent reflecting surfaces (TM-IRS) achieve directional modulation by switching their elements on and off at designed instants and durations, so that an undistorted signal reaches the intended user while appearing scrambled elsewhere. We propose a Flow Matching (FM)-based generative framework that designs these parameters for strong scrambling and reliable reception simultaneously. For orthogonal frequency-division multiplexing (OFDM) signals, we introduce a physics-informed reward that maximizes the time-modulation harmonic energy radiated in the null space of the legitimate-user response, subject to a minimum per-subcarrier signal-to-interference-plus-noise ratio (SINR), without requiring knowledge of adversary directions. Unlike prior generative flow network (GFlowNet) approaches, which assign discretized parameters sequentially, the FM model generates complete continuous configurations jointly, decoupling the generation depth from the number of elements and accelerating training. It also learns a distribution over configurations that can be resampled to vary the scrambling pattern. Numerical results show that, compared with a GFlowNet baseline, the proposed approach reduces the time to training loss near-saturation by $99.4\%$ on average for $3{\times}3$ to $12{\times}12$ surfaces, attains higher final rewards, scales to $80{\times}80$, and confines the low-symbol-error region more tightly around the legitimate user.

6G networks are expected to sense their surroundings using the same waveforms that carry data. We ask how much can be learned about the shape of an object from the terahertz echoes that user equipment transmissions produce as they scatter from that object on their way to a ceiling-mounted access point, with no sensing hardware beyond the communication link itself. We build a physics-based multistatic forward model at a 300 GHz carrier with 50 GHz of bandwidth, combining Physical Optics (PO) and Physical Theory of Diffraction (PTD) scattering with an image-source multipath lattice, molecular absorption and directional horn antennas. The model makes explicit a property of the PO mechanism hierarchy that is usually left implicit. Six physically distinct mechanisms share only three frequency exponents, so the spectral slope does not by itself determine which mechanisms a target presents and therefore cannot by itself determine its geometry. Angular diversity is accordingly a requirement for identifiability rather than an optional refinement. We compare a transmitter-count-invariant 2D-CNN with a physics-informed RBF-SVM on object orientations never seen in training. Accuracy climbs from roughly 70 percent with one transmitter to 96-97 percent with 20. The CNN gains in two regimes, irregular placement and lossy targets, while on metal under quasi-uniform placement the SVM baseline is fully competitive. Performance falls off gradually under occlusion, holding 78 percent with three quarters of the surface hidden and 63 percent when the classifier has never seen an occluded target. Multi-object scenes are limited by interference between overlapping returns rather than by angular coverage, so presence detection saturates after roughly four transmitters.

Background: Discontinuity in neonatal EEG is a critical marker of brain dysfunction but can be difficult to quantify visually. Inter-burst intervals (IBIs) are a key characteristic of discontinuity, yet their definition does not easily translate to AI detection. Objectives: To introduce low-voltage intervals (LVIs), defined exclusively by amplitude and duration, as a context-independent alternative to IBIs, develop an AI detection method for LVIs, and assess their clinical utility as a marker of brain injury. Methods: We developed a ConvNeXt model to detect LVIs and compared its performance with existing methods. We then tested the clinical relevance of detected LVIs across four applications: 1) grading background EEG abnormalities on an open benchmark dataset, 2) classifying clinical hypoxic-ischaemic encephalopathy (HIE) grades and comparing with a normal EEG dataset, 3) seizure prediction, and 4) association with seizure burden. Results: For training and validation, 654 hours of per-channel EEG from 123 neonates were reviewed and 51,162 LVIs were annotated. On held-out validation, ConvNeXt had an AUC of 0.996 and a Matthews correlation coefficient of 0.926. Model-reader agreement exceeded inter-reader agreement ($\Delta \kappa = 0.025$, 95% CI: 0.007 to 0.045, $p<0.001$), attaining expert-level equivalence. On a combined dataset of 444 neonates, percentage LVI showed a strong association with EEG grades (Spearman's $\rho=0.801$), a moderate association with HIE grades ($\rho=0.384$), a weaker association with seizure burden ($\rho=0.244$), and a strong seizure prediction capacity (AUC $=0.872$). Conclusion: AI detection of LVIs achieves expert-level equivalence, whereas quantitative features do not. Automated LVI quantification provides a robust clinical biomarker of neonatal encephalopathy associated with background EEG activity, HIE severity, and seizure risk.

This paper presents a low-complexity tensor-based channel estimation framework for downlink mmWave massive MIMO systems using passive Butler-matrix beamforming. By modeling received signals as a third-order PARAFAC tensor, we exploit known pilot structures via bilinear alternating least-squares (BALS) optimization to reduce computational overhead. The decoupled spatial factors are then processed via 1D and 2D ESPRIT to extract angles of arrival and departure with automatic pairing. Additionally, we derive the Cramer–Rao lower bound (CRLB) for this architecture. Simulations confirm the proposed estimator outperforms conventional least-squares, achieving accurate channel reconstruction and approaching the CRLB at moderate-to-high signal-to-noise ratios.

This paper introduces an enhanced channel estimation method for reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communications. Considering the presence of both the direct link from the base station (BS) to the user equipment (UE) and the indirect link via the RIS, we formulate a tensor-based receiver for joint direct and indirect channel estimation called the TenDICE algorithm. The proposed receiver relies on an extended parallel factor analysis (PARAFAC) tensor model for the received pilot signals, from which the involved (direct and indirect link) channels are estimated using a simple alternating least squares scheme. The proposed TenDICE method is compared with the state-of-the-art least squares (LS) method, Khatri-Rao factorization (KRF) method, enhanced trilinear alternating least squares (E-TALS), and the theoretical Cramér-Rao lower bound (CRLB) based on normalized mean square error (NMSE) and spectral efficiency (SE). The proposed TenDICE outperforms the LS and E-TALS methods and performs similarly to the KRF methods while estimating the direct channel.

Near-field communication with large antenna arrays requires beamforming techniques that account for spherical wavefront effects. While predefined codebooks are widely used in multiple-antenna wireless systems, the performance of randomly generated codebooks in the near-field region has not been investigated. In this work, we study four randomly generated codebooks for near-field communications: random vector quantization (RVQ), random phase (RP), random far-field (RFF), and random near-field (RNF) codebooks. We use the known RVQ gain distribution as a benchmark. For RP, we derive a large-array bulk approximation and characterize the high-gain tail behavior of the gain distribution. For RNF, we characterize the high-gain tail behavior of the gain distribution. For RFF, we derive an analytical approximation for the maximum achievable beamforming gain when far-field beams are used to serve near-field users, along with the corresponding maximizing far-field direction. We also analyze the maximum-gain scaling with codebook size. Monte Carlo simulations verify the analytical results and show that RVQ and RP degrade significantly as the array size increases, while RNF achieves the most favorable high-gain scaling. RFF can outperform RVQ and RP for finite codebook sizes, but its maximum gain saturates below one due to the phase mismatch between the far-field beams and the near-field channel. These results show how codebook structure, array size, and codebook size affect random beamforming performance in the near-field.

High-altitude platform stations (HAPS) are a promising component of non-terrestrial networks (NTNs) for wide-area connectivity. However, serving multiple users over large user footprints requires efficient power-control mechanisms that account for the unique propagation characteristics and strict power constraints of HAPS. This paper develops a tractable analytical and optimization framework for downlink power allocation in HAPS systems under statistical channel state information, 3GPP NTN path loss, and Rician fading. A closed-form expression for the signal-to-interference-plus-noise ratio (SINR) with mean-channel maximal-ratio transmission (MRT) precoding is derived, capturing geometry-dependent inter-user interference coupling. Based on this, two power-control schemes are proposed: quality-of-service (QoS)-constrained power minimization and max-min SINR fairness. The QoS solution is obtained in closed form or through a low-complexity fixed-point iteration, while the max-min solution is obtained through bisection. Numerical results demonstrate up to 6 dB reduction in required transmit power at moderate-to-high target rates and improved worst-user SINR compared to the uniform power, inverse path-loss, and geometry-aware heuristic allocations. The proposed QoS-optimal scheme also achieves a QoS feasibility probability of approximately 0.95 at R_req = 2.5 bits/s/Hz and P_max = 35 dBm, while the uniform power, inverse path-loss, and geometry-aware heuristic allocations achieve less than 0.4. Moreover, the proposed schemes achieve performance comparable to the linear programming (LP)-based benchmarks at a fraction of the computational cost, reducing the average computation time by approximately 99.97% when K = 20.

This paper proposes a tri-hybrid precoder design, cascading digital, analog, and electromagnetic processing layers, for cell-free massive multiple-input multiple-output (CFmMIMO) systems. We jointly optimize the three-layer precoders and access point (AP) transmit power to maximize global energy efficiency (GEE) under per-AP power and fronthaul constraints. To solve this, we develop an alternating optimization algorithm. The first sub-routine maximizes sum spectral efficiency (SE) for precoder design using weighted minimum mean square error, Riemannian manifold optimization, and gradient ascent over Lorentzian phases. The second optimizes power allocation via Dinkelbach's fractional programming and successive convex approximation. Convergence and computational complexity are also analyzed. Simulation results show the proposed design improves GEE by 87% and 11% over fully digital and classical hybrid precoders, respectively, at 20dBm transmit power. Moreover, joint power optimization yields 22% SE and 16% GEE gains over equal-power baselines, while outperforming colocated architectures in SE. Finally, we identify GEE-optimal regimes for AP and RF chain counts, and demonstrate that increasing dynamic metasurface antenna sizes enhances GEE in low-power regimes with negligible active power overhead.

Motion artifacts in three-dimensional photoacoustic tomography (PAT), caused by extended mechanical scanning of sparse arrays, degrade image quality. Since sparse sampling results in low-quality sub-reconstructions, tracking motion directly is challenging. While final image quality could in principle guide artifact correction, conventional regularizers do not accurately reflect photoacoustic image quality. Here, we introduce deep preferential evolution (DPE), a derivative-free optimization framework where a learned image comparator guides evolutionary search over reconstruction parameters. By learning relative quality differences between same-target image pairs rather than absolute quality scores, the comparator generalized more robustly from simulation to in-vivo images (after unlabeled domain calibration) than an absolute scorer. Comparator-based DPE successfully corrected synthetic motion across diverse anatomies and backgrounds, and it mitigated artifacts from natural, unconstrained motion in human palm images. These results demonstrate that pairwise learned comparison can provide a transferable optimization objective for high-dimensional image restoration when labeled experimental training data are scarce.

The development of multimodal sensing capabilities is crucial for realizing immersive communications and enabling rich, natural remote interactions in future sixth-generation~(6G) communications. However, transmitting high-dimensional sensory streams places substantial pressure on wireless resources and leaves a fundamental question unresolved: what information about the underlying physical process should be preserved across wireless delivery? To address this issue, we establish an event-native immersive-communication framework in which a service-relevant physical event defines the communication object, while event beliefs represent the information available to transceiver agents before and after wireless delivery. Based on this event-native framework, we further propose event-belief deficiency (EBD) to evaluate quality of experience (QoE) in immersive communications, which differs from the conventional normalized mean-squared error (NMSE), as NMSE can be insensitive to physically distinct procedures whose stream distortions lie in a similar range. For Gaussian event beliefs with mode-separable delivery over Rayleigh fading, we derive the log-det EBD, its induced received signal-to-noise ratio (SNR) threshold, the exact outage probability, and the required transmit SNR. Simulation results with a physics-driven source show that stream NMSE can remain nearly unchanged across physically distinct event procedures, whereas log-det EBD reliably reflects their event-level uncertainty. Relative to a mean-squared-error (MSE)-oriented interface, event-native delivery reduces normalized log-det EBD by up to $63.6\%$ and lowers the required average received SNR by $3.47$~dB under the evaluated outage constraint, substantiating the effectiveness of the proposed framework.

Maximum likelihood expectation maximization (MLEM) is a common approach for Poisson image reconstruction, but accurate recovery requires many iterations and is sensitive to mismatch in the assumed acquisition model. We propose a deep-unfolded penalized MLEM framework that maps a prescribed small number of iterations into trainable layers while retaining the analytical forward/backward projections and the multiplicative structure of MLEM. The unfolded architecture learns layer-dependent regularization and Poisson-model parameters, together with a data-dependent correction of the sensitivity normalization to mitigate model mismatch. We numerically show that our method improves reconstruction resolution at a fixed iteration budget and attains reconstruction quality comparable to long MLEM runs and direct data-driven models.

In this work, we propose a unified localization framework, termed UNILocMP, that combines model-based geometry and channel charting (CC) for multi-panel massive multiple-input-multiple-output (MIMO) under clock asynchronism. Owing to the multi-panel architecture, users are classified into multi-line-of-sight (LoS) users, which maintain LoS links with at least two panels, and single/non-LoS users, which maintain a LoS link with only one panel or with none of the panels. For multi-LoS users, a joint position and clock bias estimation is developed; while for single/non-LoS users, an unsupervised CC model is trained with a two-stage data augmentation strategy, where a clock-bias aware dissimilarity metric is introduced. It is numerically validated that the proposed UNILocMP outperforms model-based and CC-based baselines and achieves acceptable performance compared with fully-supervised fingerprinting. Moreover, for a fixed total number of antennas, the multi-panel architecture significantly improves localization accuracy and robustness compared with a single-panel base station (BS) deployment, particularly in the presence of clock asynchronism.

Random communication symbols allow integrated sensing and communications (ISAC) systems to exploit payload signals for sensing, but the resulting matched-filter response varies with the transmitted data. Existing statistical analyses of sensing with random orthogonal frequency division multiplexing (OFDM) waveforms have largely focused on delay and Doppler, while the spatial dimension introduced by multi-input multi-output (MIMO) arrays and precoding has received much less attention. This paper develops a three-dimensional ambiguity function (3D-AF) for random MIMO-OFDM waveforms over delay, Doppler, and angle domains. For point-target sensing, we express the 3D-AF as a quadratic form of the random communication symbols, with a deterministic matrix that captures the delay and Doppler shifts, the transmit and receive array responses, and the precoders on different subcarriers. Based on this representation, we derive a closed-form expression for the expected squared 3D-AF and show explicitly how the constellation fourth-order moment (or kurtosis) affects the ambiguity sidelobes. The same characterization yields expected integrated sidelobe level (EISL) and expected sidelobe level (ESL) metrics over mixed delay-Doppler-angle search domains. The framework is further extended to coherent multi-symbol processing with slow-time Doppler, leading to the corresponding fast-slow time (FST) approximation. These results provide a statistical characterization of the sensing ambiguity induced by random MIMO-OFDM communication signals in delay, Doppler, and angle.

Electromagnetic spectrum monitoring increasingly requires flexible analysis beyond task-specific recognition and detection. Multimodal large language models offer a unified interface, but extending vision-language models (VLMs) to raw I/Q signals requires tokenization that balances fidelity against a strict budget. For signals, dense encoding causes token costs to grow with observation length, whereas fixed-resolution compression may discard short-duration or localized signal evidence. Thus, we propose BATok, a budget-adaptive signal tokenizer that adjusts token capacity to the input length while allocating that capacity according to the signal content. BATok constructs candidate representations from signal-derived features using lightweight multi-resolution branches, then combines a local energy prior with learnable queries to resample these representations into compact signal tokens. The number of tokens adapts to the input length while remaining strictly bounded. The resulting tokens are projected into the language embedding space of VLMs. We further introduce EMSpec-Instruct, a multimodal instruction dataset aligning raw I/Q signals, waterfall images, and language supervision for modulation recognition, structured detection, and language-conditioned signal grounding. Experiments show that BATok learns effective signal representations and achieves competitive performance across all tasks.

Particle filters are a standard tool for nonlinear state estimation, but their resampling step is discrete, preventing gradient-based learning in variational sequential Monte Carlo. We introduce Differentiable Systematic Resampling (DSR), a temperature-controlled relaxation of systematic resampling, that preserves the CDF-ordered, banded structure of systematic resampling while enabling full gradient flow. DSR converges to exact systematic resampling as the temperature vanishes, and we prove a pointwise exponential convergence rate for the induced bias. Compared to optimal-transport-based differentiable resampling, DSR avoids iterative solvers and has substantially lower computational overhead. Experiments on stochastic dynamical systems and real-world handwriting data show that DSR achieves comparable or superior filtering and dynamics learning performance.

Faster-than-Nyquist (FTN) signaling can improve spectral efficiency by transmitting symbols closer together than the Nyquist limit, at the cost of additional inter-symbol interference (ISI). In this letter, we study the finite-alphabet symbol-wise achievable information rate (AIR) of FTN signaling for BPSK, QPSK, and 16-QAM under a transmit-power constraint and a common minimum mean square error (MMSE) channel-shortening (CS) detection framework, cross-validated for BPSK against an independent reduced-state Ungerboeck Bahl-Cocke-Jelinek-Raviv (BCJR) benchmark. Our results show a clear trend. Lower-order constellations can benefit from more aggressive time acceleration, while higher-order constellations become less tolerant of acceleration and achieve their best spectral efficiency closer to the Nyquist limit. At a reference signal-to-noise ratio (SNR) of 6 dB the optimum acceleration factor shifts from $\tau^\star\approx0.65$ for BPSK to 0.75 for QPSK and 0.90 for 16-QAM. We further show that this trend is preserved in coded systems and remains robust to the detector memory. The impact of FTN operation on the instantaneous-to-average power ratio (IAPR) is also examined. Overall, the results highlight how constellation order should be considered when selecting the operating point for FTN signaling.

Access control in Internet of Things (IoT) sensor networks usually acts above the physical layer: the fusion centre decides which reports to accept, while the radio of a compromised node keeps occupying the shared channel. This paper places the entitlement in the waveform: wavelet packet division multiplexing (WPDM) builds the channel as a binary tree of orthogonal pulses, and each sensor's reputation sets the depth of the subtree it may use: trusted sensors hold wide subbands, and low-trust sensors hold narrow subbands that the fusion centre withdraws one at a time. We prove that the allocation isolates sensors exactly under perfect timing, and that under a timing offset the energy leaking out of the lowpass leaf falls fourfold per tree level. The locally optimum detector of out-of-subtree transmission in impulsive Class-A noise weights each leaf in proportion to this predicted leakage. We bound the time needed to confine a compromised node and derive the optimal tree depth. In simulation, with demoted nodes radiating within their allocation, the scheme lowers the probability of a false global decision by 68% relative to fixed-allocation WPDM and to OFDM with the same reputation tiers, and by 44% relative to cryptographic re-attestation, which leads once more than 24% of the sensors are compromised.

Pinching-antenna (PA) systems provide spatial reconfigurability for integrated sensing and communication (ISAC) through controllable radiation locations along dielectric waveguides. This paper investigates a dual-waveguide PA-assisted ISAC system in which the communication user also serves as the sensing target, coupling the communication and sensing links through a common user/target geometry. Spatially separated transmit and receive waveguides provide separate spatial control over the forward and return sensing paths. We jointly optimize the transmit and receive PA positions and transmit power to maximize the aggregate communication rate subject to minimum sensing-SNR and two-dimensional localization Cramer-Rao bound (CRB) requirements, along with power, energy, and deployment constraints. To solve the resulting non-convex problem, we develop an alternating optimization algorithm that combines a bounded water-filling power update with a proximal first-order joint PA-position update. Numerical results show that the proposed design achieves higher communication rates than the considered benchmarks, with more pronounced gains under stringent localization requirements.

Integrating over-the-air computation (AirComp) with non-orthogonal multiple access (NOMA) enables concurrent data aggregation and information transmission, but tightly couples computation accuracy with communication quality of service (QoS). This paper investigates a pinching-antenna-assisted uplink, where flexible pinching-antenna (PA) placement provides additional spatial degrees of freedom to manage this coupling. We minimize the AirComp mean-squared error (MSE) subject to NOMA QoS, transmit-power, and PA deployment constraints by jointly designing the AirComp transceiver, PA positions, and successive interference cancellation (SIC) order. Exploiting the recursive structure of uplink SIC, we establish an exact NOMA feasibility condition and derive an optimal SIC-ordering rule. These structural results eliminate the NOMA power variables and the combinatorial search over decoding orders, reducing the original problem to a continuous optimization problem. We then develop an iterative algorithm with optimal AirComp transmit-coefficient and receive-scaling updates and a feasibility-preserving majorization scheme for PA placement. The proposed algorithm preserves NOMA feasibility while generating a monotonically nonincreasing computation-MSE sequence. Numerical results demonstrate the computation-accuracy gains of jointly optimizing the AirComp transceiver and PA positions under NOMA QoS requirements.

Combining a reconfigurable intelligent surface (RIS) with a receive antenna array is a promising low-complexity architecture for multi-user uplink reception, but its performance analysis for more than two users has remained an open problem: the zero-forcing (ZF) signal-to-interference-plus-noise ratio (SINR) no longer admits an explicit, low-dimensional closed-form expression, and its distribution is analytically intractable for design purposes. This paper addresses this gap for a K-user, K-antenna uplink system in which a large-scale, L-element RIS, partitioned into K user-dedicated sub-surfaces, precedes ZF reception at the base station. Through an asymptotic analysis in which the sub-surface sizes grow without bound, we show that the orthogonal projector underlying the ZF SINR converges to a rank-one matrix aligned with the desired user's channel. We use this convergence to derive a closed-form asymptotic approximation for the average per-user SINR that depends only on deterministic channel parameters. Treating this expression as a tractable design objective, we prove that equal partitioning is approximately sum-rate-optimal at leading order regardless of path-loss asymmetry across users. We also develop a low-complexity greedy pairwise-transfer search that refines the partition beyond this leading-order optimum. Monte Carlo simulations across a range of system and RIS sizes confirm that the closed-form SINR tracks the exact simulated rate closely once the RIS is large relative to the number of users. The greedy search also yields consistent, if modest, sum-rate gains over equal partitioning, validating the theory as both an accurate performance predictor and a practical design tool.

Detecting intra-operative speech impairment during awake craniotomy is essential for preserving language function. However, automated detection remains challenging because operating-room recordings contain substantial acoustic interference, clinically relevant speech events are rare, and available cohorts are small and heterogeneous across speakers. This study presents a systematic component-wise evaluation of a pipeline for distinguishing dysarthric from no-trouble speech in the DATABRASE corpus of awake-craniotomy recordings. The pipeline incorporates speaker diarization to isolate patient speech, a multi-view representation combining handcrafted acoustic descriptors with multilayer wav2vec 2.0 embeddings, speaker-conditional normalization and transferability-based feature selection to improve cross-speaker robustness, and a cascaded classifier comprising a gradient-boosted first stage and a neural second stage. Evaluation was conducted under strict speaker-independent conditions using leave-one-speaker-out cross-validation. The results show that cross-speaker performance is influenced more strongly by the speech representation than by classifier choice. The AUCs of three classifiers differed by no more than 4.7%, whereas replacing conventional acoustic descriptors with the multilayer self-supervised representation produced AUC improvements of 18.2%-26.1%. Diarization-conditioned feature extraction and the proposed classifier cascade provided additional consistent gains. These findings indicate that reliable patient-specific speech isolation and strong pretrained representations are more important than increased classifier complexity in low-resource intra-operative settings. They also quantify the potential performance gains that may be achieved through patient-specific preoperative calibration.