Papers

Blind Source Separation(BSS) is a fundamental problem in signal processing, aiming to separate multiple source signals from their mixtures without prior knowledge of the sources or the mixing process. Traditional approaches, such as Independent Vector Analysis (IVA) exploits statistical independence of sources. Recently, diffusion-based approaches have emerged as a promising alternative by leveraging powerful generative priors. Among them, ArrayDPS formulates BSS problem as a posterior sampling problem, and utilizes a pretrained speech diffusion model to guide the recovery of clean source signals. A key factor behind its separation capability is the multi-channel consistency (MC) objective, which enforces the estimated source signals to reconstruct the observed microphone mixtures through the estimated acoustic transfer functions. However, the number of microphones in the array is often limited, which constrains the performance of ArrayDPS. To address this issue, we propose VM-ArrayDPS, a novel method that augments the microphone array with virtual microphones with higher-SNR, these microphones can offer extra MC constraints to enhance the separation performance. Experimental results demonstrate that VM-ArrayDPS significantly outperforms ArrayDPS on both 2-speaker and 3-speaker datasets, showcasing the effectiveness of virtual microphone augmentation in improving BSS performance. We also did ablation studies to show the influence of the number of virtual microphones and weight of the MC objective brought by virtual microphones.

Electromyography-to-Speech (ETS) synthesis is typically a non-linear, chaotic dynamical system. However, no prior work has studied the chaotic behavior of ETS synthesis to date. Yet, prior works strictly rely on standard reconstruction metrics with parameter-heavy transformers that systematically over-smooth natural acoustic dynamics. To close this gap, for the first time, we propose a chaos-inspired Lyapunov-derived activation function (LyRIC) with two novel chaotic loss functions, Lyapunov Exponent Regularization and Multi-Scale Detrended Fluctuation Analysis, to explicitly capture the deterministic chaos of human phonation. In addition, we introduce a compressed novel encoder, GLAME, which synergizes global Mamba state-space modeling with localized GLARE attention. We comprehensively perform frame-level acoustic evaluation in a multilingual and multi-speaker setup using English and Mandarin datasets. The proposed system outperforms the established baseline with a 4.69x increase in objective intelligibility (STOI: 0.61 vs. 0.13) and a 2.08x improvement in spectral reconstruction (LSD: 1.08 vs. 2.25). Importantly, this improvement is achieved with 73.49% fewer parameters (14.34M vs. 54.10M), establishing a new baseline for ETS synthesis. To the best of our knowledge, this is the first work demonstrating that integrating non-linear chaotic physics into neural networks yields superior yet compact inductive biases for real-time ETS synthesis.

The problem of fast Doppler counter-aliasing for short-dwell weather-radar measurements is addressed. The proposed approach realizes a log-periodic slow-time design by changing only the waiting time between otherwise unchanged frequency-modulated continuous-wave (FMCW) chirps. The fast-time waveform, bandwidth, and range-processing chain are preserved while the acquisition changes the Doppler ambiguity structure. We formulate a novel moment-space ambiguity function for distributed weather spectra. It measures statistical ambiguity jointly in mean Doppler velocity and spectral width and expresses the competing sidelobes through their expected relative likelihood. A complex Gaussian process (CGP) maximum-likelihood estimator reconstructs the spectrum from the measured aperiodic samples. A deterministic basin-selection and optimization strategy accelerates the same CGP likelihood without defining a different statistical estimator. Real precipitation measurements establish central-branch stability over an extended search domain. Using 64 slow-time sweeps, we compare three acquisitions and their associated estimators. Periodic data are processed by the discrete Fourier transform (DFT) and parametric spectral estimation (PSE), log-periodic data by the nonuniform discrete Fourier transform (NUDFT) and CGP, and staggered data by velocity-difference and CGP estimators. The differences become more pronounced when the dwell is reduced to 32 sweeps. The results show that aperiodic time sampling enables extended-domain branch identification without degrading spectral-width estimation.

The computational cost of Whittle maximum-likelihood parametric spectrum estimation (PSE) for short-dwell weather-radar Doppler moment retrieval is addressed. PSE can reduce finite-sample bias, especially for short coherent processing intervals and broad spectra, but repeated likelihood evaluations make direct use expensive. A fast implementation of the finite-sample Gaussian Doppler model is introduced by writing the likelihood in terms of closed-form autocorrelation lags and synthesizing the model spectrum and analytic derivatives by fast Fourier transforms (FFTs) on the same N-point grid as the periodogram. The estimator is extended to jointly estimate total power, normalized mean Doppler frequency, and normalized spectral width. Monte Carlo simulations with 1000 noise realizations show that the fast PSE keeps the accuracy advantage of the original PSE, while objective-level timing confirms the reduction from dense lag-to-frequency synthesis to FFT-based evaluation.

This paper presents a non-orthogonal multiple access (NOMA)-enabled integrated sensing and communication (ISAC) framework for low-altitude wireless networks (LAWN), where an aerial access point simultaneously serves ground users and senses aerial targets. To improve both communication efficiency and sensing reliability, a matching-based user-target pairing (MUTP) scheme is proposed, in which strong and weak channel NOMA users are first paired into NOMA groups and then assigned to sensing targets according to a sensing-aware pairing criterion. Furthermore, an adaptive power allocation (APA) strategy is developed to balance communication quality-of-service requirements and target detection performance. The proposed MUTP-APA scheme is compared with fixed greedy pairing, exhaustive search pairing, and conventional ISAC benchmark schemes under both fixed power allocation and APA. Simulation results show that our proposed scheme achieves higher sum rate, improved worst-target detection probability, and lower communication and sensing outage probabilities compared with conventional ISAC and fixed pairing baselines in NOMA-ISAC LAWN. Moreover, the proposed method closely approaches the exhaustive search benchmark while avoiding the high computational complexity.

Continuous cardiac monitoring outside clinical settings requires signals that are both informative and practical to collect during daily life. Electrocardiography (ECG) provides rich information about cardiac rhythm and waveform morphology, while wearable photoplethysmography (PPG) is easier to acquire continuously but is only an indirect cardiovascular measurement and is highly sensitive to motion. We present BeatFlow-ECG, a conditional rectified-flow model for reconstructing single-channel ECG from synchronized PPG and inertial measurements. BeatFlow-ECG models reconstruction as conditional transport from noise to ECG using a convolutional encoder-decoder with a transformer bottleneck and explicit flow-time conditioning. Motion information is incorporated through IMU-derived conditioning features, motion-dependent loss weighting, and an easy-to-hard training curriculum. We evaluate the model under leave-one-subject-out protocols on PPG-DaLiA and WESAD. BeatFlow-ECG achieves the best results among the evaluated deterministic, adversarial, and diffusion-based baselines across all reported waveform and beat-timing metrics, with Pearson correlations of 0.983 and 0.986 and R-peak F1 scores of 0.946 and 0.955, respectively. Compared with Conditional DDPM-1D, L1 error decreases from 0.085 to 0.062 on PPG-DaLiA and from 0.074 to 0.055 on WESAD. Additional analyses on PPG-DaLiA show higher correlation in fixed R-peak-relative waveform regions and lower reconstruction error across low-, medium-, and high-motion subsets.

The growing demand for diverse sixth-generation (6G) applications requires efficient connectivity, seamless service continuity, and extended network coverage. Although terrestrial infrastructures can effectively serve most wireless user equipment (UE), communication reliability may be significantly degraded in remote or blocked environments. To address this challenge, this paper proposes a reconfigurable intelligent surface (RIS)-assisted space-air-ground integrated network (SAGIN) operating in the upper mid-band (FR3) spectrum. The considered network consists of a terrestrial base station (BS), a low Earth orbit (LEO) satellite, multiple terrestrial RISs (TRISs), and unmanned aerial vehicle-mounted RISs (ARISs), where the BS serves as the primary access node, while the LEO satellite provides complementary connectivity when terrestrial links are unavailable or cannot satisfy the required quality-of-service (QoS) requirements. Within this framework, we formulate a hierarchical RIS association problem to maximize the achievable network sum rate under RIS capacity constraints. The resulting optimization problem is a mixed-integer nonconvex problem due to the binary association variables and the coupling among UEs competing for limited RIS resources. To efficiently solve the problem, we develop a hierarchical many-to-one matching-based RIS association framework. Specifically, UEs with satisfactory terrestrial connectivity are first associated with TRISs, whereas UEs that cannot be reliably served by the terrestrial network are subsequently associated with ARISs through the non-terrestrial network. Simulation results demonstrate that the proposed hierarchical association framework significantly outperforms greedy and random association schemes in terms of the achievable network sum rate.

Reliable wireless systems depend on accurate uplink channel estimation for precoding, scheduling, and link adaptation at the base station. In the upper mid-band, wide bandwidths and large antenna arrays increase the channel dimension and make pilot-efficient estimation particularly challenging. While uplink data is available as every user transmits it, sounding resources are shared across all users in the cell, leaving only a few pilots for each user. Thus, channel estimation heavily depends on prior information. Existing approaches obtain such priors in different ways, each with limitations. Ray tracing requires a site model, channel knowledge maps rely on measurements at or near the locations they represent, and learned channel distributions do not explicitly capture propagation geometry. We therefore propose TRaffic-Aided Channel Estimation (TRACE), which builds the prior from uplink traffic without additional sounding. TRACE learns a geometry-aware channel prior from routine multi-UE uplink traffic and combines it with the available pilots for channel estimation. In a ray-traced street canyon at 15 GHz, TRACE achieves -7.48 dB NMSE with 8 pilots, outperforming three state-of-the-art priors and two geometric ablations by more than 3 dB, while remaining effective with only 2 pilots. Within a deployment, TRACE remains robust across SNRs, carrier frequencies, and reduced mapping traffic without retraining.

Magnetic field-based positioning is a resilient positioning technology that requires no external infrastructure and is hard to jam at scale. To facilitate research on magnetic field-based positioning for aerial platforms operating close to the Earth's surface, an open dataset is presented with measurements collected using an unmanned aerial vehicle carrying two optically pumped magnetometers, a type of quantum magnetometer, and a global navigation satellite system-aided inertial navigation system. The dataset includes measurements for both magnetic-field map learning and validation. Along with the dataset, a probabilistic framework for magnetic-field map learning and validation is presented and used to illustrate how the dataset may be used. Finally, we outline research directions that may be explored using the dataset.

Accurate and low latency channel estimation is critical for modern MIMO systems, particularly under mobility, where channels exhibit structured sparsity and strong temporal correlation. This paper proposes a time-series conditioned diffusion framework for channel estimation that performs denoising in the angular domain. Starting from least squares (LS) observations, we train a diffusion denoiser whose conditioning information is encoded by a long short-term memory (LSTM) network over a short observation sequence, enabling the model to exploit temporal dynamics beyond per-snapshot estimation. To robustly balance observation fidelity and learned generative priors across a wide signal-to-noise ratio (SNR) range, we introduce a learnable SNR-gated late-fusion shortcut that injects the network input into the final decoding stage through a sigmoid gate with trainable center and scale. To reduce inference latency, we adopt deterministic denoising diffusion implicit model (DDIM) style reverse updates with SNR-adaptive truncation and step allocation, which significantly reduces the number of reverse diffusion steps at high SNR while maintaining strong performance in low SNR regimes. Simulations on time-evolving standardized channel models demonstrate that the proposed method achieves consistent performance gains over existing diffusion-based channel estimation baselines, while retaining low latency through SNR-adaptive inference.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted backscatter non-orthogonal multiple access (NOMA) network operating as the secondary system of an underlay cognitive radio network. A multi-antenna base station serves several user clusters through the RIS, each containing a backscatter device (BD). We maximise the weighted sum rate of the secondary network by jointly optimising the transmit beamformers and the RIS reflection matrix, subject to the power budget, the interference-temperature limit and minimum rate requirements. The non-convex problem is solved by alternating optimisation, using semidefinite relaxation and sequential rank-one constraint relaxation. Interference-normalised user ordering makes the successive interference cancellation constraints redundant. A closed-form equal-rate power split is derived; it agrees closely with simulation and costs about 4.5% of the sum rate. Supporting the BDs reduces the NOMA sum rate by 8.7% relative to a no-BD design, while a larger BD reflection coefficient raises the backscatter rate with little NOMA loss.

In sixth-generation (6G) networks, supporting ultra-reliable low-latency communication (URLLC) services imposes stringent requirements on reliability, latency, and efficient resource utilization. The upper mid-band (FR3) spectrum has emerged as a promising candidate due to its favorable trade-off between coverage and bandwidth availability. However, achieving high reliability in dense deployments remains challenging due to interference and propagation impairments. To address these challenges, this paper investigates a reconfigurable intelligent surface (RIS)-assisted network operating in the FR3 band. We formulate a joint transmit power allocation and user equipment (UE)-RIS association problem to maximize network reliability under finite blocklength constraints. The resulting problem is nonconvex and combinatorial due to interference coupling and binary association variables. To solve this problem efficiently, we propose a multiphase resource allocation framework that integrates a successive convex approximation (SCA)-based power optimization scheme with a matching theory-based UE-RIS association algorithm. Simulation results show that the proposed scheme outperforms greedy and random baselines and achieves near-optimal performance close to the exhaustive search scheme.

Sliding-scale insulin is the default hospital response to high blood glucose. Its correction factor was designed for patients who make no insulin, yet it is routinely ordered for patients whose pancreas still works, including those on high-dose steroids. We treat the sliding scale as what it is, a proportional controller sampled every four hours, acting on a patient who already contains a working glucose controller of her own. We test it in a non-diabetic adolescent on high-dose corticosteroids and parenteral nutrition, and in 4,995 virtual patients. It fails for three structural reasons, and no choice of correction factor fixes any of them. First, the patient's own loop settles in 11.5 minutes while injected insulin takes 64 minutes to peak, so most of each dose simply replaces insulin she would have secreted; in simulation the ordered scale moved her mean glucose by less than 1.5 mg/dL. Second, one unit lowers glucose twice as much at 110 mg/dL as at 190, so any correction factor strong enough to work when glucose is high overshoots when it is low; in 99.5% of virtual patients no constant factor does both. Third, four-hour sampling is far below the Nyquist rate, so successive readings are uncorrelated and each dose responds to a value that does not persist. Even the argument that correction doses rest the beta cells, taken on its own terms, favors continuous delivery. We propose replacing the scale with a continuous insulin infusion co-delivered with the dextrose and adjusted once a day on mean glucose, the one quantity four-hour monitoring can measure.

Integrated sensing and communication (ISAC) is a key technology for next generation wireless networks, requiring highly sensitive, compact receiver front ends with multiband coverage. Rydberg atomic receivers (RARs) provide a promising architecture that satisfies these requirements owing to their high sensitivity and continuous frequency tuning capability. Unlike in classical ISAC systems utilizing orthogonal resource allocation, executing concurrent multi band tasks within a single atomic ensemble relies on shared energy state transitions. Owing to this shared structure, the communication and sensing tradeoff in RAR-based ISAC introduces an atomic-response tradeoff beyond the classical subspace and deterministic-random tradeoffs. This paper studies a multi-branch RAR (MB-RAR) architecture for joint communication and radar sensing. By solving the five level Lindblad master equation at the local oscillator (LO) biased operating point and linearizing the communication and sensing radio frequency (RF) signals as small perturbations, we obtain the double-sideband dynamic signal response of the MB-RAR. The model reveals two intrinsic constraints. First, increasing the coupling laser strength broadens the response bandwidth but can weaken the atomic response. Second, strengthening one branch's LO can suppress the other branch's conversion gain through the shared Rydberg state, even when the latter's LO bias remains fixed, which is termed the inter band gain inhibition (IBGI). Based on these mechanisms, we formulate the achievable Cramér–Rao bound (CRB)-rate region and develop a rate-constrained joint search over coupling strength and LO power allocation. Numerical results validate the proposed response model and show that joint optimization provides a more complete characterization of the achievable CRB-rate region.

Learning-based semantic communication is vulnerable to adversarial perturbations introduced before semantic encoding or over wireless channels. This paper proposes TwinViT-DeepJSCC, a preventive-corrective semantic image transceiver operating under a fixed channel-use budget. Two Vision Transformer (ViT)-based deep joint source-channel coding (DeepJSCC) branches learn complementary latent representations protected by sensitivity-aware masking. At the receiver, confidence-aware fusion, blind corruption-severity estimation, and signal-to-noise ratio (SNR)-severity-conditioned denoising diffusion implicit model (DDIM) purification mitigate residual corruption without requiring attack metadata. Experiments on the Canadian Institute for Advanced Research 100-class (CIFAR-100) dataset consider fast gradient sign method (FGSM), projected gradient descent (PGD), natural evolution strategies (NES), and Carlini-Wagner (CW) source-domain attacks, as well as random jamming and channel-aware adversarial waveforms over additive white Gaussian noise (AWGN) and block-flat Rayleigh fading. Under matched channel-use and attack budgets, TwinViT-DeepJSCC achieves maximum peak signal-to-noise ratio (PSNR) gains of approximately 9.5 dB under 20-step PGD and 10.8 dB under channel-aware waveform attacks over block-flat Rayleigh fading. Under PGD, it also improves Top-1 accuracy by up to approximately 38 percentage points over the undefended baseline and 13 percentage points over the strongest competing defense. Ablation results confirm the complementary contributions of the proposed transmitter- and receiver-side mechanisms.

Reconfigurable pixel antenna arrays provide additional electromagnetic (EM) degrees of freedom for wireless transmission by enabling hardware-level adaptation of radiation responses. In practical arrays, inter-element mutual coupling (MC) alters the switch-state-dependent responses, causing MC-unaware designs to be mismatched with the realized coupled-array channels. To address this issue, we propose an MC-aware EM and signal-processing co-design framework for downlink multi-user multiple-input single-output (MU-MISO) systems. The full pixel antenna array is modeled as a unified multi-port circuit network that jointly characterizes the feed ports and switch-controlled pixel ports. By combining the impedance matrix extracted from Computer Simulation Technology (CST) Studio Suite simulations with the full-array open-circuit far-field responses, we construct a switch-state-dependent EM-domain channel that explicitly captures both intra-antenna pixel coupling and inter-element MC. Based on this model, we formulate a sum-rate maximization problem over the binary switch-state vector and the transmit precoder. To address the resulting mixed-integer non-convex problem, we develop an alternating optimization algorithm in which the binary switch-state vector is updated via sequential exhaustive block optimization (SEBO), while the transmit precoder is optimized using fractional programming (FP). CST-based simulations show that the proposed MC-aware design outperforms MC-unaware, MC-free, and conventional patch-array baselines. These results demonstrate that accurate MC modeling is essential not only for reducing model mismatch, but also for exploiting the coupled embedded responses of practical pixel antenna arrays.

This paper investigates the joint electromagnetic (EM)-domain and baseband (BB)-domain beamforming for integrated sensing and communication (ISAC) systems enabled by pattern- and polarization-reconfigurable pixel-antenna arrays. A multiport-network-based antenna model is developed to map each binary switch configuration to its radiation response using the impedance and open-circuit radiation data extracted from Computer Simulation Technology (CST) Studio Suite simulations. Based on this model, polarization-aware virtual channels are constructed for downlink communication and statistical target sensing, leading to a mixed-integer nonconvex problem that maximizes the average sensing signal-to-noise ratio (SNR) subject to user signal-to-interference-plus-noise ratio (SINR) and transmit-power constraints. To handle the coupled binary and continuous variables, we propose an alternating optimization algorithm, in which the EM-domain switch-state matrices are updated using successive exhaustive Boolean optimization (SEBO), the BB-domain precoder is optimized via a minorization-maximization (MM) and second-order cone programming (SOCP) procedure, and the sensing combiner is obtained from a Rayleigh-quotient update. Simulation results show that the proposed design achieves higher sensing SNR and stronger target-angle robustness than the fixed-patch benchmark.

The definition of the sixth generation mobile communication is in the full swing. Mobile communication aims for the convergence of physical, human and digital world. Research project 6G NeXt is considering two demanding use cases, holographic communications and drones anti-collision system, which set heterogeneous requirements on the communication as well as the computing infrastructure. In both use cases, the clients are widely spread in the network and are cooperatively interacting with each other. Especially for holographic communication, also high processing power is required. This makes a high-speed distributed backbone computing infrastructure, which realises the concept of split-computing, inevitable. Furthermore, tight integration between processing facilities and wireless network is required in order to provide adequate quality of service to the users. This paper illustrates the use case scenarios and their requirements. Afterwards, an appropriate solution approach to realise those is elaborated. Here, the novel technological approaches are discussed based on the developed overall communication and computing architecture.

Dynamic blockage severely degrades vehicular integrated sensing and communication (ISAC), since narrow millimeter-wave beams must be adjusted before the line-of-sight (LoS) link is blocked or misaligned. This paper proposes WM4ISAC, a proactive ISAC framework built on a world model that learns a reusable latent dynamics model of the traffic environment from downlink radar heatmaps, rather than directly mapping observations to beam indices. The proposed framework uses a vision (V) module to compress sensing observations, an action-conditioned memory (M) module to predict latent environment evolution, and lightweight event heads to evaluate LoS alignment, blockage, and scatter-assisted non-line-of-sight (NLoS) alignment for candidate beams. A codebook-level controller then uses these event predictions to proactively switch between LoS and NLoS beams without knowing the future user or blocker states. Simulations under dynamic blockage show that WM4ISAC improves the mean rate by $28.9\%$ over current LoS tracking, raises the 5%-tile rate from $0.9$ Mbps to $30.5$ Mbps, and reaches $88.7\%$ of the perfect-information upper bound.

In time-varying D-optimal subset selection, the number of replaced coordinates does not measure physical displacement: when the optimal selection for a set of mobile sensors changes, a single sensor may have to cross the entire graph. We therefore measure movement by a bottleneck matching distance on a fixed mobility graph, and define the distance margin at radius P as the smallest old-objective loss among configurations farther than P from the current maximizer. Our main result shows that if the projector drift is small and the distance margin at a chosen radius exceeds an explicit threshold, then every maximizer of the new objective lies within that radius, without tie-breaking rules or uniform conditioning assumptions over subsets. Moreover, we show that separation information of this kind is genuinely needed: a weighted-path construction shows that, at a fixed baseline eigengap, an arbitrarily small perturbation can move an optimal sensor from one end of the graph to the other, so no bound depending only on perturbation size and eigengap can vanish with the perturbation. We also give a finite certificate, conditions under which local and Voronoi restrictions retain a global optimum, and oversampled and regularized extensions, and we identify the learned dictionary criteria to which the results apply.

Fourier-domain beamforming methods, such as filtered backpropagation (FBP), are beneficial in ultrafast plane-wave imaging due to their computational efficiency and high image quality. These methods use the Fourier diffraction theorem (FDT) for steered plane waves to exactly invert linear scattering models. However, these models rely on simplifying assumptions that degrade image quality. The use of linear transducer arrays, in particular, requires: (i) a two-dimensional space, and (ii) lossless tissues. Herein, Laplace-domain beamforming is proposed. This method retains the computational efficiency of the Fourier-domain methods while improving image quality for linear arrays. Specifically, the method uses a generalization of the FDT, here called Laplace diffraction theorem, to account for three spatial dimensions, finite array element heights, and lossy tissues. A phantom experiment showed that the proposed method improves image uniformity, spatial resolution, and contrast compared to FBP. Using three steering angles, the lateral and axial -6 dB-widths of wires reduced by up to 12% and 25%, respectively, while the contrast of anechoic regions improved by up to 9%.

This work presents a prototype implementation of a noncoherent multiple-input multiple-output (MIMO) system based on Grassmannian signalling, where neither the transmitter nor receiver requires channel state information (CSI), operating at a carrier frequency of 15 GHz in the frequency range 3 (FR3) upper mid-band envisioned for 6G. To the best of our knowledge, this is the first prototype implementation of Grassmannian signaling reported in the literature beyond sub-6 GHz bands. We identify the motivation of applying Grassmannian signaling, a form of unitary space-time modulation (USTM), in this band for wireless and mobile applications with low spectral-efficiency requirements but strict reliability and power-efficiency demands. With this idea in mind, we present a system design integrated with orthogonal frequency-division multiplexing (OFDM) that addresses the challenges associated with the higher carrier frequencies of FR3 while providing reliable operation across a broad range of scenarios, including urban and rural environments as well as vehicular communications. We then present the prototype implementation of the proposed design, based on software-defined radio (SDR) devices integrated with radio-frequency (RF) mixers, filters, and power amplifiers to ensure adequate operation in the targeted band. Over-the-air (OTA) experiments based on symbol error rate (SER) measurements under non-line-of-sight (NLoS) and line-of-sight (LoS) scenarios, and across different data rates, show good agreement with software simulations, thereby validating the prototype implementation.

High precision continuous localization is a fundamental challenge for automated guided vehicles (AGVs) operating in electromagnetically sensitive industrial environments. To mitigate the continuity limitations of conventional discrete localization methods and the accuracy limitations of existing continuous localization methods, this paper proposes a continuous AGV localization method that integrates interferometric fiber Bragg grating (FBG) array sensing with a hybrid deep learning framework. We first design a shallowly buried interferometric FBG array sensing track to enable the stable acquisition of high-quality signals. The received sensing signals are subsequently parameterized and preprocessed in a task-oriented manner, in which a multichannel joint representation is constructed using a sliding time window. A hybrid deep learning framework is developed to estimate AGV position from multichannel FBG response signals by jointly exploiting inter-channel spatial correlation and temporal continuity in the measurement sequence. Experimental results show that the proposed method achieves a mean absolute error (MAE) of less than 2 cm under complex industrial conditions, proving its effectiveness and robustness in practical AGV localization scenarios.

Movable antenna (MA) has emerged as a promising technology to unlock spatial degrees of freedom for wireless sensing systems. However, optimizing MA positions for multi-target sensing remains challenging due to the complicated coupling between the array geometry and unknown target directions, as well as severe sidelobe interference. In this paper, we propose an MA-enabled multi-target wireless sensing scheme to achieve both high-precision and robust target parameter estimation. To characterize the sensing performance while maintaining tractability, we introduce two complementary metrics, namely a lower bound (LB) of the Cramér-Rao bound (CRB) and the steering vector correlation (SVC). Specifically, by investigating the partial order relationship of the Fisher information matrix, we derive a closed-form LB of the multi-target CRB, which decouples the unknown target directions. Meanwhile, the SVC is employed to characterize the spatial correlation and sidelobe-induced interference among sensing signals over different directions. Based on these two metrics, we formulate an MA position optimization problem that minimizes the SVC while constraining the LB below a predefined threshold. To render the resulting non-convex problem tractable, we exploit the spatial symmetry of the continuous SVC function and discretize the sidelobe region, thereby transforming the spatial minimax objective into a finite set of constraints. Meanwhile, the LB constraint is equivalently reformulated as a spatial variance constraint on the MA positions. Building on these transformations, we develop a successive convex approximation (SCA) algorithm within an alternating optimization (AO) framework to iteratively optimize the horizontal and vertical MA coordinates.

This paper studies waveform-level security for Integrated Sensing and Communication (ISAC). Instead of relying on spatial beamforming or power allocation, we randomize the sensing waveform itself using phase keys and tangent Artificial Noise (AN) applied to a Fourier-curve constellation. Coordinated legitimate nodes know the key and recover the sensing reference, while Eve observes only a randomized waveform and cannot construct the correct coherent matched reference. We consider an Unmanned Aerial Vehicle (UAV)-mounted Eve with a dominant Line-of-Sight (LOS) link who fully exploits favorable geometry, including a bypass scenario where Eve listens to both the direct and echo sides to circumvent our design. We further consider a stronger Eve that attempts phase-key reconstruction from long-term observations; even then, residual key mismatch structurally degrades correlation and AN cancellation, while accurate key recovery incurs high computational complexity. Numerical results show that the proposed design reduces Eve's probability of detecting the sensing reference using non-coherent detection and, in the bypass case, substantially suppresses her correct-delay lock probability, while the legitimate receiver reliably locks onto the reference through echo accumulation.

Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinically interpretable measurements. Deep learning has advanced this task but remains dependent on costly expert annotations. Label-efficient strategies such as self-supervised pretraining and semi-supervised learning are expected to ease this burden, yet it remains unclear whether they yield reliable delineation and whether the deep models they produce outperform the delineation tools used in practice. We address this in two stages. First, comparing self-supervised objectives with supervised or semi-supervised fine-tuning across one internal and four external datasets, we find that pretraining helps but the objective matters, and that the value of semi-supervised fine-tuning depends on the pretraining objective. Second, we benchmark the selected deep learning model against widely used open-source (NeuroKit2, Prominence, ECGdeli) and commercial (CalECG) tools using three complementary metrics. The model ranks best on every metric and dataset, outperforming the strongest tool by a clear margin on the rhythm-diverse set (mIoU 71.3 vs. 54.8%; averaged point-wise sensitivity 92.6 vs. 76.4%), and degrades the least from sinus to arrhythmia. A rhythm-stratified and point-wise analysis further characterizes the distinctive behavior of each tool, yielding practical guidance for tool selection. These results provide systematic, multi-dataset evidence that self-supervised pretraining is effective for ECG delineation and enables a label-efficiently trained deep learning model to outperform widely used delineation tools by leveraging abundant unlabeled data. This supports adopting such models in diverse, real-world clinical settings.

Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information. Existing SemS formulations typically operate open loop: sensing configurations and observation schedules are fixed before inference and cannot respond to evolving task-level evidence. We propose Agentic SemS, a closed-loop framework for AI-enabled radio access networks (AI-RANs) that controls sensing within a communication-feasible profile set. A profile-conditioned causal Transformer updates the semantic belief from streaming observations, while key-value caching enables efficient state updates across profile changes without repeatedly processing the complete history. A semantic utility network estimates the task-level benefit of acquiring the next observation block under each feasible profile after accounting for sensing cost. The resulting continuation utilities jointly support next-profile selection and semantic early exit, adapting sensing configuration and duration to evolving evidence. The expected semantic gain is further related to conditional mutual information, providing a value-of-information interpretation of continued online sensing. Experiments on Widar3.0 with six emulated sensing profiles show that, in comparison with full-sequence High, the resource-efficient Agentic setting reduces normalized cumulative sensing cost by 25.33% while achieving 85.79% Macro-F1. At the same utility checkpoint, semantic early exit provides a further 12.35% cost reduction over adaptive sensing without early exit, with a 0.97-percentage-point Macro-F1 decrease.

Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroencephalogram (EEG). However, ESI is fundamentally ill-posed because source activity is substantially higher-dimensional than sensor observations, resulting in non-unique solutions. Recent learning-based approaches address this ambiguity by learning data-driven source priors, yet they often struggle to generalize across subject-specific cortical geometries. To address this, we propose CANDLE, a learning-based ESI model that estimates source activity on subject-specific cortical geometries. CANDLE learns a prior over the null space induced by the source-to-sensor mapping derived from T1-weighted MRI, restricting learning to unobservable source components while preserving geometric constraints. To train CANDLE, we develop a whole-brain simulator spanning over 1,100 subject-specific cortical geometries with source configurations derived from over 26,000 statistical brain maps. Trained exclusively on simulated data, CANDLE outperformed prior ESI methods on simulated source activity estimation and generalized to two empirical tasks: (i) intracranial stimulation localization from simultaneously recorded scalp EEG and (ii) epileptogenic zone estimation from presurgical interictal EEG. Our project page is available at https://candle-esi.pages.dev}{https://candle-esi.pages.dev.

Many deep learning based radar target detectors rely on range-cell level labels for training, which are expensive to obtain. To reduce the labeling burden, this paper presents a training strategy that uses only range-window level labels. Specifically, two sub-echoes are randomly cropped from the same window echo, resulting in a known relative shift between them. A siamese Transformer is then used to identify target-salient responses from one sub-echo and form pseudo labels, which are mapped to the paired sub-echo according to the known shift. In addition, a shift-equivariant consistency (SEC) constraint is imposed to align the predicted responses of the two sub-echoes after shift compensation. By exploiting shift priors within each labeled window, the proposed approach learns reliable detection without requiring cell-level labels. Experimental results show that it consistently surpasses classical constant false alarm rate (CFAR) detectors and achieves performance close to range-cell level labels training, while substantially reducing labeling cost.

We present a comparative analysis of two promising filter bank multicarrier waveforms for integrated sensing and communications (ISAC) systems operating in high-mobility environments. In particular, we consider two-dimensional FFT-precoded filter bank (2D-FFT FB) modulation and the recently-proposed affine filter bank modulation (AFBM) waveforms, which exploit different signal representations across the time-frequency, delay-Doppler and affine domains to improve robustness against Doppler effects, each resulting in distinct features. On the one hand, 2D-FFT FB improves spectral localization under high Doppler conditions, while AFBM combines chirp-based modulation with filter bank processing to achieve low peak-to-average power ratio (PAPR) and reduced out-of-band emission (OOBE). These schemes are compared in terms of OOBE, PAPR and bit-error-rate (BER), as well as the delay and Doppler cuts of the ambiguity function (AF) under deterministic and random sensing symbols. Results for orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM) are also included to provide additional basis of comparison. The results show the performance trade-offs between the two filter bank (FB)-based schemes, providing insights into waveform candidates for high-mobility ISAC scenarios such as vehicular communications and future sixth generation (6G) networks.