Papers

The monopulse technique determines the angle of a target by comparing signals from two narrow beams, yielding a precise angular estimate with low complexity. However, it struggles to resolve multiple closely spaced scatterers within the same resolution cell. Existing methods for estimating multiple scatterer angles involve complex signal processing and system modifications. We propose an effective method to estimate the angular centroid of scatterers using the mode of monopulse angle estimates. A semi-analytic expression for the angle estimate distribution is derived, confirming that its mode aligns with the centroid. To enhance estimation accuracy, we employ frequency diversity to reduce sample correlation. Numerical results validate the advantages of the proposed method, demonstrating superior performance over conventional techniques with low complexity.

The evolution of wireless networks toward sixth-generation (6G) is driving demand for intelligent, flexible, and energy-efficient propagation control. Reconfigurable holographic surfaces (RHSs) enable fine-grained wavefront manipulation through densely packed programmable elements. This paper introduces shape-adaptive RHSs, which dynamically change surface geometry to provide extra spatial degrees of freedom. The adaptive design responds to environmental dynamics, mitigates blockages, and improves performance across urban communications, aerial coverage, and joint sensing and communication scenarios. We propose a network architecture that jointly designs the surface shape at the RHS and optimizes transmission. Simulation results show that the proposed shape-adaptive RHSs significantly increase data rates. These findings establish shape-adaptive RHS as a foundational technology for intelligent, context-aware wireless environments in 6G and beyond wireless networks.

Movable antenna (MA) systems improve the channel by repositioning antennas, but each repositioning interrupts data transmission for a time set by the largest displacement and consumes actuator energy that accumulates over all displacements. Existing designs optimize the positions within a single transmission block, whereas the movement energy is limited over the long term and the positions chosen in one slot constrain those reachable in the next. We study online throughput maximization under the movement delay and a long-term energy budget. Using the Lyapunov drift-plus-penalty framework, we convert the long-term problem into per-slot problems and solve each by a proximal minorization-maximization algorithm that requires no channel statistics and keeps an antenna stationary whenever its relocation is not worth the energy. We prove that the budget is met for every channel realization and that the algorithm attains, within a controllable penalty, the throughput gain of reallocating the budget over time. Simulations show that this reallocation improves the throughput over imposing the budget slot by slot when the demand for repositioning is concentrated in time, whereas the latter, supported by the same solver, is preferable when the channel varies continuously, at the cost of an order of magnitude more antenna actuation.

Autonomous vehicles increasingly rely on high-end multi-frequency GNSS receivers that track modernized civil signals, such as L2C, L5, and L1C, alongside legacy GPS L1~C/A. Although multi-frequency operation improves robustness against natural error sources, its resilience against synchronous multi-band spoofing attacks remains insufficiently characterized. This paper presents a method for quantifying the inter-band alignment accuracy required for a coherent spoofer to evade cross-frequency consistency monitoring that a resilient receiver may employ. As a foundational study, the presented method is implemented and evaluated using the GPS L1 C/A and L2C signal pair within a controlled software-defined environment. This environment encompasses deterministic signal synthesis, programmable inter-band code offsets realized through fractional-delay filtering, and processing by a dual-frequency GNSS-SDR receiver. An inter-frequency pseudorange-difference consistency detector is defined and calibrated using a constant-false-alarm-rate (CFAR) approach, yielding a threshold of approximately 19 m at a false-alarm probability of $10^{-3}$. Signal fidelity and receiver capture are verified through correlation-function analysis using a multi-tap correlator bank. The inter-band code offset is then swept from one code chip to 0.005 chips to characterize detector performance. Results show that a perfectly aligned coherent attack yields a detection probability of zero, while detection probability increases rapidly with inter-band misalignment, reaching approximately 50% at 0.05 chips (about 15 m) and approaching unity for offsets of 0.2 chips or greater. These results quantify the alignment precision required for successful coherent dual-frequency spoofing and establish a practical framework for future evaluation of L5, L1C, and hardware-based multi-frequency spoofing scenarios.

Motion correction in magnetic resonance fingerprinting (MRF) helps preserve the accuracy of quantitative maps; however, existing approaches provide motion updates only every 7-8 seconds. We propose a navigation framework that integrates compact k-space navigators throughout the MRF acquisition, enabling sub-second motion estimation at minimal sequence overhead. A 3D spiral-projection MRF sequence was augmented with three orthogonal spiral navigators inserted every 0.5 seconds, enabling motion estimation by comparing navigator signals with quantitative scout (Q-Scout) data, i.e., motion-free low-resolution k-space with matching contrast evolution. The Q-Scout is obtained via a rapid calibration during the dummy preparation period, incurring no additional scan time. Motion estimation is formulated as dictionary matching in a discriminant subspace with optimization refinement. The method was evaluated in simulation and in vivo for 1 mm isotropic brain 3D MRF at 3 T. Across 35 motion-corrupted acquisitions with motion-free references available, the proposed motion correction reduced MRF reconstruction normalized root-mean-square error (NRMSE) by 7.1% and increased the structural similarity index measure (SSIM) by 0.085. Motion estimates aligned with 8 second temporal-rate image-based navigation (mean absolute difference of 0.15 mm and 0.23 degrees), while the proposed method provided higher temporal resolution and improved motion correction. The proposed framework enables robust motion navigation in MRF at 0.5 second temporal resolution with minimal sequence overhead. By using contrast-consistent modeling and efficient inference, it improves the reliability of quantitative MRI under rapid, unpredictable motion.

This study presents the development and in silico evaluation of an Integrated System Architecture (ISA) for the physiological control of continuous-flow ventricular assist devices (VADs). The system employs an automatic controller based on pressure measurements at the VAD inflow cannula to estimate heart rate and ventricular filling pressure, enabling dynamic speed adjustments that optimize VAD-patient interaction. The evaluation encompassed 27 simulation scenarios assessing four aspects: operating speed regulation, responsiveness to variable demand, adverse event mitigation, and physiological impact. The controller continuously adapted to preload, afterload, and heart rate, accommodating flow demands with speed adjustments ranging from -10% (-600 rpm) to +43% (+2,600 rpm) relative to the baseline (6,000 rpm). Furthermore, dynamic regulation eliminated ventricular suction and backflow events, which were previously observed in 12 of the 27 uncontrolled scenarios. Hemodynamic and metabolic outcomes demonstrated improved left ventricular ejection fraction (+6.23% to +62.31%), reduced ejection work (-1,658.64 to -206.37 mmHg*mL), decreased pressure-volume loop area (-9,485.17 to -1,056.05 mmHg*mL), and reduced myocardial oxygen consumption (-20.5 to -1.14 mL/min). Concurrently, total oxygen delivery increased (+56.9 to +626.23 mL/min) along with cardiac power (+100 to +3,070 mW). These findings demonstrate that the ISA successfully maintains physiological regulation and mitigates adverse events, establishing a solid foundation for future in vitro validation.

We introduce Graph Neural Automata Clustering (G-NAC), an unsupervised clustering method in which observations interact as cells on a fixed neighborhood graph. A shared recurrent graph-neural cellular rule evolves latent domain states through local interactions, which are converted into a rank-based spectral affinity for partitioning. Across 73 clustering tasks from 57 benchmark datasets, G-NAC achieved a mean adjusted Rand index (ARI) of 0.7951, comparable to Genie at 0.7941 and higher than the other evaluated baselines. Empirical training time and GPU memory scaled approximately linearly from 5,000 to 100,000 nodes. Learned transition rules also transferred from smaller source graphs to independent 100,000-node samples generated under matched conditions. These results demonstrate a recurrent graph-clustering formulation while identifying dependencies on graph quality, readout design, and source-target similarity.

Spherical harmonic encoded acoustic sound-fields capture directional characteristics of room impulse responses that are useful for accurate spatial audio reproduction. However, high costs of multi-microphone measurements and numerical simulations motivate alternative data-set augmentation and synthetic data generation methods that supplement small collections. This paper introduces time-varying exponentiated convolution methods that transform both Gaussian noise and impulse responses into reverberation and modified spectral-decay fields respectively. We derive two recursive and fast convolution algorithms that extend into the spherical harmonic domain, model smooth reverberation time distributions with non-stationary Gaussian processes, and realize an optimal filter design. Experiments evaluate computational performance, and validate out-of-distribution generated impulse responses.

Localising artifacts in synthetic speech remains challenging, as most evaluation methods yield only global quality scores. This paper presents XSQ-AST, a framework that combines the SQ-AST speech quality model with WhisperX phoneme alignment and multiple saliency methods to produce temporally localised artifact diagnostics without model retraining. Saliency maps are projected onto continuous distributions via kernel density estimation and onto phoneme boundaries via phoneme-discretised saliency maps. A 40-participant listening test validated the framework across five perceptual dimensions. Attention Rollout, Attention Flow and an adapted GradCAM produced temporal distributions that correlated with listener highlights, with different methods best suited to different artifact types. An AUC-ROC analysis confirmed discrimination above chance.

Recent years have witnessed a growing interest in AI-aided Kalman filters. While emerging methodologies, such as KalmanNet, were shown to facilitate tracking in partially known state-space models, they are not directly applicable when the underlying dynamics is unknown. To overcome this limitation, we extend the KalmanNet philosophy to the unknown-dynamics regime by developing blind Kalman filtering frameworks that learn both the predictor and the correction gain from data, assuming that the state-evolution function and the noise statistics are both unavailable. To this end, we first introduce a task-aware neural Kalman filtering framework, Blind-KalmanNet, which carries the learning principle of the Kalman gain into the prediction step through a two-head neural architecture that jointly learns a state-dependent linear surrogate and the Kalman gain from data. Building on this formulation, we then develop our main framework, Koopman-aided Blind-KalmanNet, which incorporates Koopman operator theory to lift the unknown dynamics into a latent space where the state evolves linearly. The lifted linear predictor integrates seamlessly into the Blind-KalmanNet structure: the pre-trained deep Koopman network serves as a globally structured predictor, augmented by the task-aware residual surrogate and the learned Kalman gain inherited from Blind-KalmanNet. Extensive experiments demonstrate that the proposed frameworks achieve competitive performance against baselines, with Koopman-aided Blind-KalmanNet attaining the best accuracy across all considered settings.

We propose a deterministic smoothed maximum a posteriori (SMAP) framework for high-dimensional multiple-input multiple-output (MIMO) detection. The discrete constellation prior is approximated by an i.i.d. Gaussian mixture, yielding a differentiable MAP objective that retains the constellation structure while enabling continuous optimization. Unlike sampling-based approaches, SMAP formulates detection as a deterministic optimization problem and can be initialized by the output of an arbitrary detector, allowing it to operate either as a standalone detector or as a refinement stage. For hard detection, we introduce SMAP-KR, which complements the continuous solution with a local $K_R$-neighbor search evaluated according to the original maximum-likelihood metric. Annealed continuation improves robustness for higher-order constellations, while a two-neighbor approximation reduces the cost of evaluating the mixture prior. We further develop a soft-output SMAP method in which a local Gaussian approximation based on the curvature of the smoothed posterior provides symbol probabilities and bit log-likelihood ratios without posterior sampling or explicit counterhypothesis lists. Numerical results for critically loaded MU-MIMO systems show that SMAP-KR provides an increasingly favorable performance-complexity tradeoff as the system dimension grows and can also effectively refine the output of existing detectors. For coded transmission, soft SMAP provides substantial block-error-rate gains over both LMMSE- and K-best-based soft detection.

High-speed GPU cluster interconnects are evolving from 200 to 400 Gbps to meet the growing computing demands of artificial intelligence. We investigate high-order modulation and advanced equalization for 400 Gbps interconnects. Theoretical bit error rate (BER) expressions for 4/8-ary pulse amplitude modulation (PAM) and approximate expressions for six PAM6 formats generated from 32-ary quadrature amplitude modulation (32QAM) are derived. In additive white Gaussian noise, PAM6 generated by framed-cross 32QAM achieves the best BER performance among the PAM6 formats. PAM6 and PAM8 incur peak signal-to-noise ratio (PSNR) penalties of approximately 4.2 and 7.2 dB, respectively, relative to PAM4 at the KP4 forward error correction (KP4-FEC) limit. Simulations compare PAM4, PAM6, and PAM8 at a line rate of 440 Gbps (net rate of 400G) under different bandwidth limitations. As bandwidth becomes more constrained, bandwidth requirements increasingly dominate performance over noise tolerance. PAM4 requires the lowest PSNR to reach the KP4-FEC limit in mildly bandwidth-limited channels, whereas PAM6 performs best under moderate limitations. PAM8 offers a significant advantage under severe limitations because of its lower bandwidth requirement and reduced susceptibility to high-frequency attenuation. With maximum likelihood sequence estimation, PAM6 and PAM8 reach the KP4-FEC limit under all evaluated channel conditions, while PAM6 requires a lower PSNR in most channels, highlighting its potential for 400G-SerDes systems.

This paper investigates energy-efficient power control in 6G in-X subnetworks. We consider a graph neural network (GNN) framework that captures inter-subnetwork interference and the underlying wireless topology to optimize transmit powers. Three energy efficiency (EE) formulations are studied: (i) network-centric, which maximizes total network energy efficiency; (ii) subnetwork-centric, which maximizes the average energy efficiency per subnetwork; and (iii) a multi-objective approach, which balances energy efficiency and sum-rate performance. Extensive simulations in industrial factory settings with 3GPP channel models demonstrate that the GNN effectively learns interference-aware power allocation policies, significantly outperforming maximum power transmission and existing GNN based power control solution. Results showed network EE gains of up to 1341%, average per-device EE improvements of up to 1302%, and sum-rate enhancements up to 24.7% relative to a maximum transmit power policy, depending on the chosen optimization formulation and trade-off settings.

Radio frequency fingerprint identification (RFFI) is a promising approach to distinguish physical wireless devices using hardware-induced signal imperfections. Conventional RFFI methods only provide discrete device labels and no human-interpretable representation of the relations among received signals. We propose supervised device charting, which maps location-insensitive channel-state information (CSI) fingerprints to a low-dimensional chart that visualizes cluster compactness, overlap, and outliers. We evaluate the method with real-world 5G New Radio (5G NR) measurements from six commercial smartphones and introduce the neighbor label error rate (NLER) to quantify class-separation accuracy. Our results demonstrate that two- and three-dimensional device charts provide an interpretable visualization of the learned RFFI representation. For three-dimensional device charts, the NLER is 0.22% for same-day measurements and 7.38% for measurements from the next day. The device charts reveal a cross-day distribution shift and map the held-out device close to the known device of the same model. Increasing the device chart dimensions further improves cluster separation at the expense of interpretability.

The growing capability of Wi-Fi devices to identify human activities using channel state information (CSI) raises privacy concerns. To counter this threat, we propose GRAW, an adversary system, acting as a privacy defender, that degrades the human activity recognition (HAR) system at the user device by perturbing the router's signals that the device uses to estimate CSI. GRAW employs generative adversarial imitation learning (GAIL) to construct perturbation signals, and thereby eliminates the need for any information on the target HAR systems and their inputs (i.e., zero-knowledge operation). We evaluate GRAW against seven representative HAR models, using datasets collected in five environments, including our own dataset. We observe that GRAW is the only remote attack scheme that degrades every tested HAR model to a random-selection level. At the same perturbation level, GRAW achieves an attack success ratio up to 76.7% higher than comparison methods, while maintaining over 99% packet success rate on regular Wi-Fi communication. We demonstrate the feasibility of GRAW through real-time, over-the-air experiments with software-defined radios.

Orthogonal time frequency space (OTFS) modulation is well suited for high-mobility communications, but channel estimation and efficient multi-user transmission remain challenging. In this paper, we develop an OTFS-rate splitting multiple access (RSMA) framework that jointly considers pilot transmission, channel estimation, message detection, and resource allocation under imperfect channel state information (CSI). We consider guard-based and superimposed transmission for the common and private messages to account for the tradeoff between channel-estimation reliability and spectral efficiency (SE). We derive a linear minimum mean square error (LMMSE) channel estimator, error-aware message-passing detectors, and tractable surrogate rate expressions that account for channel-estimation errors. Based on these expressions, we formulate a joint resource-allocation problem and develop an SCA-based two-dimensional search algorithm together with a low-complexity alternative that exploits the analytical structure of the resource allocation. Numerical results demonstrate reliable channel-estimation and BER performance and show that configurations with guard-based common-message transmission achieve favorable sum-SE performance, while the preferred private-message structure depends on the operating regime. The low-complexity algorithm achieves near-SCA performance with substantially reduced computation time, and OTFS-RSMA remains competitive with OTFS-NOMA while requiring only a single SIC stage.

Spectrum cartography is crucial for spectrum-aware resource management in low-altitude networks, where uncrewed aerial vehicles (UAVs) collect measurements to reconstruct power spectral density (PSD) radio maps. However, limited UAV energy budgets constrain both sampling density and onboard computation, while complex urban blockages hinder reliable measurement transmission to a remote station. To address these challenges, we propose an air-ground cooperative framework for active online spectrum cartography. Through adaptive bandwidth allocation, the UAV actively collects PSD measurements using uncertainty and simultaneously transmits them to a mobile uncrewed ground vehicle (UGV), while the UGV maintains a reliable air-ground link for map updates. Specifically, we first develop an online deep-unfolded tensor decomposition method to accelerate online map updates. We then derive a bit-depth-dependent interpolation-error model to guide the selection of quantization bit depths to balance overhead and accuracy. Finally, we use uncertainty information to coordinate the UAV and UGV for active sampling and link maintenance, respectively. Extensive experiments show that the proposed reconstruction method achieves a nearly 27-fold speedup over online tensor decomposition. In urban scenes, the proposed framework outperforms the representative baselines, reducing both normalized mean squared error (NMSE) and outage ratio by more than 23% each and increasing the packet-service completion ratio by over 4%.

Sixth-generation (6G) wireless systems are envisioned as networks of networks, integrating diverse in-X subnetworks that provide localized, high-performance connectivity. Ensuring reliable communication in dense deployments, such as industrial robots and vehicles, is challenging due to dynamic interference and strict performance requirements. Traditional radio resource management (RRM) methods have limitations, prompting the need for AI-based solutions. In this paper, we address the challenges of dynamic resource allocation and scheduling in 6G in-X subnetworks supporting applications with heterogeneous characteristics by proposing a novel framework that combines Multi-Agent Reinforcement Learning (MARL), Federated Learning (FL), and Explainable AI (XAI). Our solution is designed to improve the reliability, robustness, and transparency of resource management and intra-subnetwork scheduling while ensuring data privacy and fairness across multiple co-existing subnetworks. Unlike existing works, our approach considers a realistic scenario with multiple devices per subnetwork, thereby offering a more comprehensive and scalable solution. The proposed explainable RL framework enables agents to collaboratively optimize channel allocation and scheduling without the need to share raw data, preserving the privacy of each participating subnetwork. Extensive simulations based on 3GPP scenarios demonstrate the effectiveness of our approach, showing significant improvements in performance, and transparency over existing solutions.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising paradigm for future wireless networks, providing user-centric services and cooperative coverage. By using unmanned aerial vehicles (UAVs) as aerial access points, CF-mMIMO networks can exploit UAV mobility to enhance three-dimensional (3D) coverage and spectral efficiency (SE). However, most existing studies on UAV deployment for communication optimization typically assume that UAVs follow ideal point-mass motion (IM), neglecting flight-control constraints and finite-horizon position errors in real flight execution. Consequently, IM-trained deployment policies may degrade severely or become difficult to execute in practice. Motivated by this, we model each UAV as a six-degree-of-freedom quadrotor rigid body with a cascaded flight controller to capture the impact of flight-control-constrained motion (FM) on communication optimization. Based on this model, we formulate a joint UAV 3D deployment and power allocation problem under FM to maximize the downlink average SE in CF-mMIMO networks. To address this problem, we propose LERE, a large language model (LLM)-enhanced multi-agent reinforcement learning (MARL) framework. In LERE, the LLM evolves hybrid rewards with both global and local components via multi-level feedback. The evolved hybrid rewards guide MARL policy optimization and promote multi-UAV cooperation. Experimental results demonstrate that LERE achieves higher SE than reward-design baselines while substantially reducing UAV position errors. Notably, when tested under FM execution, the FM-trained LERE policy achieves a 60.49% SE gain over its IM-trained counterpart, confirming the necessity of incorporating flight-control constraints into UAV-enabled CF-mMIMO optimization.

The integration of sensing into communication networks introduces a new privacy risk, as a passive eavesdropper (Eve) may exploit ISAC data signals as signals of opportunity to perform unauthorized sensing of targets. In this paper, we develop a sensing-privacy-enhancing geometric constellation shaping (GCS) framework for OFDM-ISAC systems. The key observation is that constellation-dependent ranging performance is receiver-specific. For matched filtering at Eve, the ranging MSE is governed by the constellation kurtosis $\kurt$, whereas reciprocal filtering at the legitimate receiver (Alice) is governed by the inverse second-order moment $\ism$. Based on closed-form MSE expressions, we define sensing privacy as the ranging MSE gap between Eve and Alice and characterize its dependence on these two moments. The analysis shows that positive skewness of the symbol-power distribution is necessary for a positive intrinsic moment gap, namely $\kurt-\ism$. We further derive an exact skewness-based decomposition of the intrinsic moment gap and a canonical two-ring characterization, providing analytical guidelines for privacy-enhancing constellation geometries. We then formulate Eve-aware and Eve-agnostic GCS designs that balance sensing privacy and communication reliability through the minimum Euclidean distance (MED), with the Eve-agnostic design depending only on the intrinsic moment gap. Numerical results demonstrate scalable privacy–communication trade-offs, while over-the-air experiments show that the proposed constellation shaping substantially increases the ranging error gap between Eve and Alice with only a small communication throughput loss.

Generative models of neural activity could help characterize tissue dynamics, compare experimental conditions, and simulate population activity for applications ranging from disease and drug-response studies to closed-loop experimentation. Existing approaches, however, typically assume a fixed set of sorted neurons, whereas high-density microelectrode arrays produce extremely sparse, array-wide binary spike volumes in which the observed subset of electrodes varies across assays. We introduce a discrete generative model that represents this activity using a shared vocabulary of spatiotemporal motifs. A residual vector-quantized autoencoder learns the motif vocabulary, while a factorized masked transformer predicts where activity occurs and which motif appears at each active location. We evaluate the model on 31 assays spanning human brain organoids and acute ex vivo human hippocampal tissue. The learned motifs are broadly reused: assay identity explains only $9%$ of the entropy in motif use, and motif overlap across tissue types is comparable to overlap within them. When representation quality is evaluated independently of the generative prior, our approach achieves $5.2\times$ the voxel-level reconstruction average precision of a matched flat tokenizer. For masked completion and free generation, the full model achieves $1.4$–$2.6\times$ the site-level average precision of the matched generative baseline and outperforms it across all four families of generation metrics. These results establish a compact, reusable representation for array-wide spiking activity without learned assay-specific parameters, providing a scalable foundation for generative modeling across diverse neural preparations.

Inferring network topology from noisy node observations is a central problem in graph signal processing. In this paper, we consider Laplacian-constrained graph estimation for Gaussian Markov random fields, focusing on the underdetermined regime in which the number of samples is smaller than the number of graph nodes. Existing approaches often formulate the problem as a sparsity-regularized maximum-likelihood estimation problem. While effective, such methods typically require iterative optimization and are often computationally demanding, particularly under Laplacian constraints. Instead, we propose a non-iterative estimator of graph Laplacians that uses effective resistance for regularization, and evaluate the method using a simple sparsification procedure. Experiments show that with some trade-off in edge and weight recovery on the considered dataset, computational cost for moderately sized graphs can be substantially reduced.

Ultra-wideband (UWB) provides absolute spatial constraints for indoor positioning, but these constraints become insufficient when multiple radio links are blocked or unavailable. A calibrated sensing fiber, in contrast, provides a fixed spatial topology that can be activated by distributed acoustic sensing (DAS) events independently of radio propagation. Motivated by this complementarity, we present FTALoc-FGO, a topology-aware fixed-lag factor-graph framework for fiber-assisted localization under UWB degradation. Along-fiber DAS events are mapped through the calibrated topology into anisotropic spatial observations, where a single fiber layer provides a directional constraint and two orthogonal layers provide complementary two-dimensional information. Fisher-information analysis characterizes this topology-dependent information and motivates the directional and joint fiber factors used in the fixed-lag graph. Experiments combining measured DAS events with simulated UWB ranges under controlled degradation evaluate the method over 30 paired realizations. Under complete UWB loss, FTALoc-FGO reduces the causal outage RMSE by 32.6% and 66.4% for single- and dual-layer routes, respectively, relative to robust UWB-only FGO. These results demonstrate that calibrated fiber topology can provide complementary spatial support when radio-based observations become insufficient.

Ambisonics Upscaling (AU) aims to enhance the spatial resolution of sound fields by estimating high-order Ambisonics (HOA) components from low-order observations. While deep learning and model-based strategies have been considered for AU, both approaches exhibit significant performance degradation in realistic scenarios, where reverberant sound fields violate the directional sparsity inherent to discriminative mappings. In this work, we address AU as a generative task rather than a deterministic reconstruction, expanding generative modeling to specifically target the recovery of spatial information in reverberant speech. We investigate two dominant continuous-time generative paradigms, adapting both Score-based Generative Model and Flow Matching to these complex acoustic settings. We provide an extensive numerical study comparing our methods against state-of-the-art baselines in various acoustic scenarios. Additionally, we conduct subjective listening tests to evaluate the perceived quality and spatial accuracy of the proposed generative framework across various reverberant scenarios. The studies reveal that Flow Matching consistently outperforms both its discriminative counterparts and Diffusion-based paradigms in all reverberant settings.

Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage, and multipath, while visual observations provide complementary spatial context. This paper presents a vision-wireless fusion framework for multi-user localization using pilot-indexed channel state information (CSI). Orthogonal pilot indices preserve the identities of the communicating UEs in the CSI token sequence and localization outputs. The model encodes each pilot-indexed CSI observation as a query token and uses cross-attention to retrieve user-specific information from spatial visual memory. Self-attention among CSI tokens further captures inter-user interactions, while the resulting multimodal representations are used for user-wise localization. Experiments on different datasets show consistent improvements over model-based, CSI-only, and multimodal-fusion baselines. Further experiments evaluate the model under different wireless and visual conditions.

The 6–18~GHz upper mid-band is a promising spectrum range for future sixth-generation (6G) networks due to its favorable balance between available bandwidth and propagation capability, while its same-site coverage performance remains a key deployment concern. This paper presents a wideband channel measurement campaign at 13 spatially aligned frequency points from 6 to 18~GHz in an urban macrocell (UMa) environment, and investigates the frequency evolution of channel characteristics and same-site coverage capability. The results show that path loss generally increases with frequency, while the higher-frequency channels tend to exhibit smaller root-mean-square (RMS) delay spread (DS) and larger PDP-based $K$-factors. Based on the measured path-loss characteristics, the additional link-budget requirement and service-aware same-site coverage are further evaluated. Under the baseline service configuration, the 80% coverage distance $d_{80}$ decreases from approximately 129~m at 6~GHz to 58~m at 18~GHz. Further analysis quantifies the impacts of service rate, resource allocation, interference, and link-budget degradation on coverage, providing measurement-based insights for same-site deployment across the 6–18~GHz band.

This paper investigates robust beamforming and antenna position design for movable antenna (MA)-enabled integrated sensing and communication (ISAC) systems under antenna position errors. In particular, deviations between the actual and nominal antenna positions in practical MA arrays are inevitable due to limited mechanical accuracy. Therefore, the conventional ISAC design based on nominal positions may suffer from substantial performance degradation. To address this issue, we consider a monostatic downlink MA-ISAC system in which each transmission frame consists of a low-power pilot and an ISAC payload. The target angle, reflection coefficient, and antenna position-error vector are jointly estimated based on these two observations, thereby enabling sensing with imperfectly known antenna positions. We derive the resulting angle Cramer-Rao bound (CRB) and formulate a problem that minimizes its worst-case value subject to each user's signal-to-interference-plus-noise ratio (SINR) requirement under all possible position errors. By exploiting a common phase-response decomposition, we obtain tractable reformulations and develop an alternating optimization (AO) algorithm for the beamforming vectors and nominal antenna positions. Numerical results demonstrate that, compared with conventional nominal-position designs, the proposed robust scheme achieves improved communication reliability while maintaining competitive sensing accuracy under practical antenna position uncertainty.

Physical layer (PHY) information hiding supports critical applications, such as digital fingerprinting for transmitter identification and undetectable side channels for covert communication, and has attracted considerable attention from the research community. One category of prior studies focuses on theoretical analysis, proposing techniques such as artificial noise or reconfigurable intelligent surfaces to enable undetectable covert transmission. However, hardware prototypes are rarely presented due to their algorithmic complexity or hard-to-satisfy assumptions. Another category of studies focuses on system-level solutions, achieving PHY information hiding by customizing existing modulation schemes. However, these methods are typically designed for specific wireless protocols, limiting their generalizability. In this work, we introduce a new PHY information hiding paradigm that differs fundamentally from the previous two categories of approaches. Inspired by recent advancements in other domains such as image information hiding, we migrate encoder-decoder neural networks to the PHY information hiding field, embedding secrets by introducing imperceptible distortions within the preamble waveform. Sim-to-real fine-tuning is additionally proposed to tackle unique challenges, e.g., fading and hardware imperfections. The designed methodology is practical and protocol-agnostic. We provide case hardware prototypes of two commercially popular wireless technologies, i.e., LoRa and Bluetooth Low Energy (BLE), using commodity software-defined radio (SDR) transceivers, demonstrating excellent feasibility and generalizability.

Unobserved confounding is a fundamental challenge in causal inference from observational data. This article develops a mixture-learning perspective, viewing latent confounders as sources of heterogeneity that induce mixture structure in observed data. Under suitable structural and identifiability assumptions, recovering the mixing distribution and component mechanisms enables estimation of interventional distributions and causal estimands. Using variants of Bernoulli mixtures as a running example, we contextualize mixture-learning techniques and their structural assumptions, and connect them to causal inference in panel-data settings, including latent factor models and synthetic interventions.We then consider high-dimensional exponential-family mixtures with dependent outcome trajectories, moving beyond counterfactual means to model counterfactual distributions. We situate this perspective relative to complementary approaches for unobserved confounding. Together, these ideas provide a bridge between mixture learning and causal inference, connecting recent advances in high-dimensional mixture learning to scalable identification and estimation of causal effects while raising new challenges for mixture learning.

Machine learning (ML) has emerged as a powerful tool for soil analysis using Laser-Induced Breakdown Spectroscopy (LIBS). However, traditional black-box models often lack interpretability, limiting their effectiveness in decision-making processes. This study introduced explainable AI (XAI) techniques to enhance both the interpretability and classification performance of Partial Least Squares Discriminant Analysis (PLS-DA) models for soil classification with LIBS. Local Interpretable Model-agnostic Explanations (LIME) was employed to identify the local spectral features associated with soil elements, providing explanations for their contributions to classification outcomes. To improve classification performance on unlabeled spectra, prediction uncertainty was quantified by sequentially removing the top local spectral features identified by LIME as critical to the classification. Higher uncertainty was associated with a smaller number of feature removals needed to change the label. Spectra for which a large number of features had to be removed before the label changed were treated as high-confidence predictions and admitted to a co-training process. Thresholds on this flip count were defined separately for spectrally ambiguous and stable classes to manage uncertainty identification effectively. To address the sensitivity of PLS-DA to class imbalances, equal proportions of pseudo-labels from each class were included in co-training. The proposed method was evaluated on the publicly available EMSLIBS dataset, achieving a test accuracy of 92.69%, competitive with the best-performing methods in the literature. The XAI results provided insights into the causes of misclassifications and identified the dominant spectral features critical for accurate predictions, advancing interpretable AI solutions for LIBS-based soil analysis.