Papers

Dual-Orthogonality waveforms are multi-antenna signaling schemes that enforce mutual orthogonality across transmit channels and over a prescribed set of delay shifts. By relaxing strict time orthogonality to the physically admissible propagation region, they preserve full-band operation per transmit antenna while embedding communication data and maintaining stream separability. This makes them attractive for Integrated Sensing and Communications (ISAC), where reliable data transmission, high-resolution sensing, and imaging must coexist under time-varying propagation. In dynamic multipath environments, delay-Doppler dispersion across multiple paths perturbs the transmit subspaces and partially breaks the relaxed orthogonality conditions. This paper analyzes this effect and develops a multipath-aware decoding framework based on structured parameter estimation, effective-subspace reconstruction, and low-complexity linear equalization. Numerical results show communication performance comparable to OFDM-based ISAC and MIMO-OTFS baselines while improving sensing and imaging through full-band per-transmit operation. The proposed approach achieves approximately 30 cm range resolution, more than 15 dB suppression of multipath imaging artifacts with coherent SAR processing, and a favorable sensing-communication trade-off. Over-the-air experiments at 60 GHz validate multi-stream communication, the designed zero-correlation region, and accurate radar ranging. A second campaign in a highly reflective indoor environment further demonstrates multipath-aware stream equalization under strong unsuppressed reflections.

We consider a multistatic radio-frequency imaging problem with anisotropy, in which the reflection from a point depends on the positions of the transmit (Tx) and receive (Rx) arrays. The goal is to label the voxels of a field of view by a finite set of semantic classes and to group them into object instances. For the image formation of each Tx–Rx pair we apply a standard inverse-problem solver, and we feed the resulting per-pair reconstructions into a trained three-dimensional (3-D) U-Net that performs the fusion implicitly and the per-voxel classification explicitly. On a controlled, under-determined multistatic setup, we consider the following image formation methods: back-projection (BP) and the least absolute shrinkage and selection operator (LASSO) from a single deterministic snapshot, and incoherent BP and group-LASSO from multiple fading snapshots. For each imaging method we train a separate U-Net that fuses the six Tx–Rx pairs (its input channels) and assigns each voxel a probability vector over the classes. Taking the most probable class gives a labeled volume—the semantic reconstruction. Object instances and their oriented bounding boxes then follow by geometric post-processing (clustering and principal-component analysis). Across a wide range of signal-to-noise ratio, the semantic reconstruction (scored against ground truth by segmentation intersection-over-union) and the resulting 3-D detection degrade far more gracefully than the classical intensity reconstruction: the detection in particular stays reliable well into noise levels at which that reconstruction has dissolved. Because real scenes contain objects of classes the network was not trained on, we add an explicit unknown class trained by outlier exposure, which labels held-out novel objects as unknown instead of mislabeling them as a known class by reconstructed shape.

This paper proposes a framework for treating the inverse obstacle problem for nonlinear elliptic equations with nonlinear materials. The problem is challenging because nonlinear materials exhibit a rich diversity of scenarios to consider, since nonlinearity can take different forms. In this article, after categorizing the nonlinearities into a few fundamental classes, a dedicated imaging method is proposed for each class, derived by combining two powerful concepts: the Monotonicity Principle (MP) and the $p-$Laplace Signature (pLS). The Monotonicity Principle (MP), recently extended to nonlinear materials, provides a monotonic relationship between the material property and the measured quantity (the Average Dirichlet-to-Neumann map) that can be \lq\lq inverted\rq\rq \ to find the shape of anomalies. The $p-$Laplace Signature (pLS) allows for modelling the solution of an elliptic PDE with nonlinear materials, for large or small boundary data, in terms of a proper $p-$Laplace equation that captures the essence (the signature) of the problem. For example, pLS with $p=2$ allows the reduction of a nonlinear elliptic PDE to a linear one, providing a powerful bridge for applying imaging methods and algorithms developed for linear materials. In this contribution, the two pillars of MP and pLS are combined in new imaging methods to enlarge the class of nonlinearity that can be treated within the inverse obstacle problem. Moreover, the theoretical limits of the methods are provided in the ideal case of noise-free measurements: outer-support reconstruction when $p=2$, and convex-hull reconstruction when $p\neq2$.

Existing studies on the pinching-antenna system (PAS) assume that waveguides are deployed straight, which fails to serve communication regions with curved boundaries. To address this limitation, this paper proposes a curved waveguide-enabled pinching-antenna system (C-PAS), where the waveguide is placed along the building ceiling in an arc to maximize the line-of-sight (LoS) coverage. On this basis, the optimal pinching-antenna (PA) placement strategy and the nearest PA placement strategy are presented. The optimal strategy distinguishes between scenarios, i.e., with or without inner-wall blockage, and derives a closed-form solution for the optimal PA position to maximize the signal-to-noise ratio (SNR) received at the user. Meanwhile, the nearest strategy aligns the PA with the angular position of a user in polar coordinates, thereby accommodating the curved geometry of the region. Furthermore, the outage probability (OP) and the average rate (AR) are analyzed for each strategy, and the corresponding analytical expressions are derived, respectively. The results show that the optimal PA placement strategy achieves better OP and AR performance than the nearest strategy, particularly under large waveguide loss coefficient or waveguide height. Moreover, for a service region of fixed area, there exists an optimal sector angle or inner-wall radius that either minimizes the outage probability or maximizes the average rate. Furthermore, the choice of a waveguide bending radius is influenced by the transmit power of the base station, where a smaller bending radius is preferable at high power and the middle arc performs best at low power.

In recent years, low-power wide-area network (LPWAN) technologies have gained significant traction as a connectivity option for Internet of Things (IoT) applications. While these networks have been successful in providing long-range, low-power, and low-cost connectivity, they currently face scalability, reliability, and efficiency challenges that require immediate attention. In this paper, we first identify important challenges for LPWANs. We then advocate for the introduction of a centralized radio access network (C-RAN) architecture tailored for LPWANs and present a proof-of-concept implementation and deployment of the proposed C-RAN for the widely popular long range (LoRa) standard. We also provide experimental results to demonstrate and quantify the increased sensitivity that can be obtained from joint processing of the baseband signals of multiple receivers, enabled by the proposed centralized architecture in quasi-static scenarios and drone-mounted transmitters.

Seamless indoor–outdoor coverage conventionally relies on coordinated outdoor macro cells and indoor small cells, incurring high deployment and backhaul costs. Intelligent omni-surfaces (IOSs) provide a promising alternative by enabling simultaneous reflection and transmission across building boundaries. Enabled by recent advances in conformal antennas, conformal IOSs (CIOSs) mounted on building facades offer a flexible and deployment-friendly solution for integrated indoor–outdoor coverage. This paper develops a stochastic-geometry framework to evaluate the downlink performance of CIOS-assisted networks serving both indoor and outdoor users. Specifically, a building-dependent spatial model is established where randomly located buildings are modeled via a Boolean scheme with CIOSs mounted on their facades. Unlike planar IOS deployment, conformal integration introduces geometry dependent visibility constraints, which are captured through the effective numbers of participating elements defined by visible arc metrics for reflection and transmission. Incorporating these constraints, coverage probability expressions are derived under three CIOS operating protocols. Numerical results show that, compared with planar IOS, CIOS improves the successful connection probabilities of the reflected and transmitted links by up to 75% and 125%, respectively, while the overall coverage gain remains dependent on blockage conditions because curvature reduces the effective serving aperture.

Dynamic spectrum access must coordinate secondary users under bursty primary-user activity while preserving packet reliability and delay. We present TACAN, a centralized policy that represents each channel as a token containing occupancy history and automatic-modulation-classification entropy; a context token supplies queue class, delay and user identity. A Transformer encoder is warm-started from an occupancy-greedy policy and refined with proximal policy optimization. The frozen policies were trained to maintain a channel assignment in every slot, including when queues were empty. We therefore replay them on held-out trajectories and distinguish standby assignment success from packet-present access and packet delivery. In a 20-channel network with 60 primary devices and 4 secondary users, TACAN achieves 92.53% +/- 0.47 packet-present access success, compared with 89.94% for Greedy and 83.53% for PPO+MLP. Its paired gain over Greedy is 2.59 points (parametric 95% CI 1.89-3.29), with wins in all five seeds; the exact two-sided sign-test value is 0.0625. The gain rises from 0.57 points at normal primary-user load to 7.67 points at extreme load. TACAN also reduces mean delivery delay from 1.208 to 1.123 slots and the conditional user-reliability gap from 9.69 to 3.15 points. Delivered packets per SU-slot remain arrival-limited (30.12% versus 30.11%), so no packet-throughput gain is claimed.

Mixture-of-experts (MoE) architectures enable efficient large language model (LLM) inference at the wireless edge through sparse activation. The wireless distributed MoE (WIDE) architecture addresses edge-resource constraints by distributing experts across devices coordinated by an edge server. However, WIDE suffers from repeated uplink transmissions of high-dimensional expert outputs via orthogonal multiple access. To overcome this bottleneck, we propose AirMoE, an over-the-air computing (AirComp)-enabled framework for simultaneous expert-output aggregation via wireless waveform superposition. Integrating AirComp into MoE inference introduces three challenges: fast-varying aggregation weights, layer-dependent error sensitivity, and channel-aware expert placement. To address these challenges, we construct an inference-aware AirMoE error metric to quantify aggregation distortion effects on end-to-end (E2E) inference accuracy via perturbation-based layer-sensitivity calibration. We then formulate a joint optimization problem to minimize this error and decompose it, without loss of optimality, into a two-timescale framework. At the fast timescale, we derive a globally optimal threshold-based power-control policy that separates devices into coefficient-aligned and full-power groups. At the slow timescale, we develop an activation- and channel-aware expert placement strategy that assigns more important experts to devices with lower channel-power cost. Extensive experiments demonstrate that AirMoE outperforms representative baselines in E2E inference accuracy, especially under strong device heterogeneity.

For next-generation air interfaces, two-sided artificial intelligence (AI) models have received growing attention, with AI models deployed at both the transmitter and receiver for efficient channel feedback and data communication. However, their practical deployment is complicated by assumptions commonly made in existing studies, including isolation from legacy users, training under predefined channel conditions, and gradient-based fine-tuning requiring substantial cross-vendor communication. This article revisits these assumptions and presents practical alternatives. First, for legacy coexistence, we integrate two-sided model processing into the 5G New Radio (NR) protocol stack and validate its operation alongside conventional NR on a real-world testbed. Second, instead of training under a massive number of predefined channel conditions, we construct a compact model table by jointly optimizing two-sided models with trainable surrogate channels, and select the best model according to the current channel condition to enable channel adaptation with high task performance and low training/storage overhead. Finally, unlike existing fine-tuning that exchanges large gradient vectors containing potentially private model information, we present gradient-free zeroth-order fine-tuning that requires only scalar feedback, facilitating multi-vendor interoperability. Together, these approaches advance the practical deployment of two-sided AI models while highlighting key open challenges.

Spectrally efficient frequency-division multiplexing (SEFDM) is an attractive waveform to improve communication spectral efficiency by compressing the subcarrier spacing, yet its use for integrated sensing and communication (ISAC) poses a fundamental sensing challenge. Specifically, the intentional loss of subcarrier orthogonality generates SEFDM-induced intercarrier interference (S-ICI), which combines with Doppler-induced ICI (D-ICI) from moving targets to blur range–velocity maps and severely degrade sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) SEFDM systems, this paper develops a model-driven ISAC framework that supports spectrally efficient multi-user communication while mitigating both S-ICI and D-ICI in sensing. To this end, an intercarrier interference mitigation network (IMNet) is proposed, which exploits the distinct physical structures of the two interferences. A bank of Doppler correction filters first compensates the velocity-dependent D-ICI over multiple Doppler hypotheses, and an axial-attention network subsequently suppresses the residual D-ICI and the long-range S-ICI to recover reliable sensing signals. To further improve range and velocity estimation accuracy, IMNet with local refinement (IMNet-LR) is proposed, which performs maximum-likelihood refinement with nuisance projection around the IMNet detections to achieve sub-cell precision without an exhaustive global search. Simulation results show that IMNet-LR achieves near-maximum-likelihood range and velocity estimation accuracy with more than three orders of magnitude lower execution time compared to conventional detection methods.

Direct acquisition of global hyperspectral images (HSIs) is infeasible given contemporary hardware facilities and limited resources, while global hyperspectral monitoring is critical for remote sensing applications. A more economical approach is to interpretably convert global Landsat-8/9 multispectral images into NASA's AVIRIS-level HSIs. This conversion involves both spatial super-resolution (SpaSR, 30-m to 15-m) and the highly ill-posed spectral super-resolution (SpeSR, 7-band to 172-band), collectively referred to as dual super-resolution (DualSR), whose duality between SpaSR and SpeSR has recently been established. Existing SpeSR methods, mostly designed to reconstruct CAVE-level HSIs with only 31 visible bands, are not applicable to the AVIRIS-level task involving 172 visible, near-infrared, and shortwave-infrared bands. This motivates us to customize an interpretable alternating direction method of multipliers network (ADMM-Net) using the Woodbury W-Lemma and a spectral continuity prior. Unlike conventional generative SpaSR, we employ a panchromatic sharpening strategy to recover physically grounded spatial details. However, this strategy induces very large matrix inversions (LMIs), with dimensionality proportional to the number of pixels, even after applying the W-Lemma. We resolve this issue by designing an LMI-free proximal gradient descent network (PGD-Net). Consequently, the proposed PGD-ADMM interpretable network (PAINT) achieves substantial improvements in both computational complexity and reconstruction performance. Beyond state-of-the-art reconstruction performance, PAINT improves Landsat classification from 78.98% accuracy and 76.06% kappa to 92.16% accuracy and 90.98% kappa.

The stability of markets hosting leveraged exchange-traded products is governed not by any single product's loop gain but by the spectral radius of a loop-gain matrix, and scalar per-product monitoring underestimates system feedback by construction. Recent work measures the self-reinforcement of a leveraged fund's daily close rebalancing through a scalar loop gain and treats cross-asset spillovers as bias. We model complexes on correlated underlyings as a coupled feedback system with matrix gain L and show that scalar monitoring has two blind spots: (i) cycle amplification, since rho(L) >= max_i l_ii for nonnegative coupling, strict under two-way coupling; and (ii) transmitted displacement, which arises already under one-way coupling and is invisible to the receiver's own gain. We give a reduced-form estimator of L requiring only prices and public fund assets – no signed order flow – via cross-asset overnight reversals, reporting its measurement-convention sensitivity explicitly. In simulation the spectral radius is recovered with RMSE 0.005 at T=250, a lead-lag confounder yields a 2% false-alarm rate, and in a calibrated blind-spot configuration the scalar monitor reports "safe" and the matrix monitor "unsafe" on 100% of paths. In the 2026 Korean single-stock LETF episode we detect transmission from the SK Hynix complex into Samsung Electronics' closing price (DiD z=-2.82; exact randomization p=0.0055 against 182 control pairs), scaling with the sender's rebalancing capital; conservatively, about 41% of Samsung's closing displacement variance is imported – invisible to its own "moderate" gain of 0.24. The same estimator returns nulls for the U.S. MSTR-Bitcoin-Coinbase complex, whose capital is comparable but whose closing venue is far deeper. Monitoring should be organized around the (complex x venue) matrix, not around products.

As an increasing number of critical applications, including environmental, emergency, meteorological, and agricultural, rely on real-time anomaly detection in geospatial data streams, challenges related to the storage, processing, and communication of this data arise. Traditionally, large volumes of data have been sent to centralized processing locations for insight extraction. Given the big data context of these applications, this approach becomes increasingly infeasible as data volume and velocity continue to increase. This paper proposes a lightweight edge-oriented approach for anomaly detection and localization for geospatial data streams. By leveraging the H3 discrete global grid system and a multi-scale drill-down logic, the proposed approach significantly reduces computational overhead, achieving a 99.7% reduction in evaluations compared to traditional flat-scan methods. Furthermore, by filtering out noise-induced flickering anomalies at lower resolutions, spatially-persistent anomalous signals can be efficiently identified. The results demonstrate that the proposed framework effectively distills massive geospatial data into actionable insights.

Frequency Range 3 (FR3), spanning 7.125-24.25 GHz, is a promising candidate band for 6G communications, bridging the coverage of sub-6 GHz bands and the capacity of millimeter-wave frequencies. Its incumbent-dense and non-contiguous spectrum demands frequency-agile, interference-tolerant receivers (RXs) capable of hopping across fragmented sub-bands while withstanding strong blockers. Mixer-first RXs are well suited to this role, yet their design for FR3 has not been systematically addressed. This paper presents a hardware design perspective on mixer-first RXs for FR3, evaluating selectivity enhancement, harmonic rejection, linearization, low-noise design, and multi-phase clock generation under FR3-specific constraints. The analysis identifies viable techniques, fundamental limitations, and circuit- and architecture-level design tradeoffs. A central insight is that the frequency-translational property of mixer-first RXs allows selectivity, linearization, and noise cancellation to be implemented partly at baseband and translated to RF, enabling frequency-agile FR3 operation while shifting the dominant design constraints to mixer parasitics, baseband circuit robustness, and multi-phase clock generation.

We propose an integrated sensing and communication (ISAC) beamforming method that performs joint multiple-input multiple-output (MIMO) radar sensing and multi-user MIMO communication. Our approach builds on a matrix nearness formulation of the MIMO radar problem and utilizes our recently proposed efficient solver, where the computational complexity is dominated by an eigenvalue decomposition (EVD) evaluation at each iteration. We extend this formulation to an ISAC scenario by incorporating minimum signal-to-noise ratio constraints as communication design criteria, only requiring statistical channel state information knowledge at base station. Furthermore, we propose a method to avoid the burdensome EVD evaluations in certain iterations, reducing the computation time by up to six times in a massive MIMO setting.

This paper proposes a novel two-stage joint waveform design framework for multi-antenna Integrated Sensing and Communication (ISAC) systems that simultaneously enable Multiple-Input Multiple-Output (MIMO) radar sensing and Multi-User MIMO communication. First, a transmit waveform covariance matrix is designed by solving a convex matrix nearness problem for beampattern synthesis that simultaneously maximizes transmit power in desired directions and minimizes cross-directional correlations, while accommodating independent antenna power constraints and supporting interference suppression through radiation null steering. Second, a waveform conforming to the designed covariance is synthesized while additionally enforcing inter-user interference suppression via the zero-forcing approach and imposing practical implementation constraints, notably limiting the peak-to-average-power-ratio on the individual antenna elements. Simulation results demonstrate that the approach significantly enhances ISAC waveform design flexibility, performance, and computational efficiency compared to the alternative methods in the literature.

This paper presents a real-time 60 GHz wireless video transmission system enabled by a millimeter-wave (mmWave) Dynamic Metasurface Antenna (DMA) and offers a 60 GHz transceiver solution. The end-to-end communication system is established using off-the-shelf mmWave up/down-conversion modules (EK1HMC6350, HMC6300, and HMC6301) with a connectorized DMA-based transmit front-end and universal software radio peripheral (USRP) for digital baseband processing. The proposed framework eliminates the need for costly high-frequency signal generators and spectrum analyzers, thus simplifying implementation and validation while reducing operational costs. A real-time HD video stream at 720p resolution (1280 x 720) is transmitted and successfully retrieved over a wireless link using QPSK modulation, achieving a data rate of about 3 Mbps with high reliability. The demonstrated platform can support various 60 GHz standard physical-layer protocols, such as IEEE 802.11ad/ay and 802.15.3c. This work highlights the potential of programmable metasurface antennas as a compact, energy-efficient alternative to legacy phased arrays for next-generation 6G and beyond mmWave/sub-THz wireless communication systems, small-cell outdoor backhaul links, and RF sniffing and channel sounding.

Dual-band wireless architectures transmit the control information over the sub-10 GHz while reserving sub-THz for high data rate links, offering notable capacity gains. However, a critical bottleneck in such systems is timing synchronization. Due to the narrow beams of the sub-THz radio units (RUs), when the dual-band user equipment (UE) rotates or moves, it becomes necessary to switch the transmission between the sub-THz RUs. This switching requires recalibrating the timing of uplink (UL) and downlink (DL) transmissions to prevent communication disruptions. Moreover, for sub-THz RUs, the method introduces significant overhead and latency, especially when switches are frequent. Leveraging the reliable sub-10 GHz band offers greater resilience to UE mobility, making it suitable for control signalling. Thus, in this paper, we propose a deep learning-based algorithm that infers the propagation delay from the sub-THz RUs to the UE using sub-10 GHz channel characteristics. The inferred delay is used for calculating the timing advance for UL transmissions without the need for two-way synchronization. Simulation results show that the RU switch can be made seamless at the physical layer, without incurring any synchronization-related latency.

Radio-Frequency (RF) holographic imaging reconstructs a volumetric map of the permittivity contrast from phase-coherent samples of the scattered electromagnetic (EM) field. The resulting inverse problem is severely ill-posed, as the receivers are orders of magnitude fewer than the unknown 3D volume elements (voxels). It is classically regularized by sparsity-promoting solvers such as the Iterative Shrinkage-Thresholding Algorithm (ISTA). Two assumptions limit these solvers: the L1 penalty is spatially uniform, and the EM forward model is typically approximated as a linear operator. This paper revisits the problem through algorithm unrolling, in which the solver iterations become the layers of a compact trainable network that preserves the EM forward model and learns only a few interpretable parameters from limited data. Building on the Learned ISTA (LISTA), a Weighted LISTA (W-LISTA) is first proposed, which learns a spatially-varying L1 regularization, steering the sparsity prior towards target shapes consistent with the deployment. Second, the Low-Rank Weighted LISTA (LoRaW-LISTA) applies a low-rank adaptation (LoRa) of the holographic operator to compensate for model mismatches from linearized EM approximations. Both methods are validated on full-wave EM simulations and on a 2.45GHz indoor measurement campaign with human-body phantoms. Combining the spatially-varying regularization with the low-rank adaptation of the EM model improves the signal-to-clutter ratio by about 70% over the ISTA and LISTA baselines, recovering structural details where classical iterative solvers fail. Inference takes less than 30s to reconstruct 1m^3 of scene on conventional GPUs. The proposed tools are rapidly adaptable building blocks for the sensing layer of emerging smart radio environments.

Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild label collection. The high inter-and intra-subject emotional variability motivates us to explore WER modeling through graph node classification in a limited resources learning scheme powered by Self-Supervised Learning (SSL) graph masking augmentation tasks. We employ a subgraph sampling approach during training, utilizing labeled and unlabeled data, along with supervised, semi-supervised, and SSL mechanisms in a multi-task inductive graph neural network architecture. Our evaluations on K-EmoPhone through leave-one-group-out cross-validation in the binary arousal and valence tasks yield average accuracy gains of 4.3% and 7.8%, compared to the full resource setting, utilizing only 20% and 25% of the labels, respectively. Our model analysis sheds light on the relation of SSL graph augmentations to emotional arousal and valence and justifies the approach of SSL-driven subgraph training for in-the-wild WER.

Direction-of-arrival (DOA) estimation is a fundamental problem in array signal processing, for which the Cramer-Rao bound (CRB) serves as a standard performance benchmark under both stochastic and deterministic source models. In multipath environments, the source matrix is of low-rank and, more specifically, exhibits a within-group proportionality structure. Although stochastic CRBs for coherent DOA estimation have been studied, theoretical results for their deterministic counterparts remain limited. This paper aims to bridge this gap. We first derive a tangent-space condition characterizing when structural constraints on the source matrix can strictly reduce the frequency CRB. We then prove that, for uniform linear arrays, imposing only the low-rankness of the source matrix leaves the frequency CRB unchanged, although it reduces the source-matrix CRB whenever the rank constraint is nontrivial, whereas fully exploiting the coherence structure yields a strictly smaller frequency CRB for almost all parameter values under mild conditions. These conclusions are further extended to analytic array manifolds and multidimensional DOA models, with componentwise results established for uniform planar arrays. The results demonstrate that performance gains in DOA estimation arise from the detailed coherence structure rather than low-rankness alone. Numerical experiments are finally provided to validate the theoretical findings.

Stacked intelligent metasurfaces (SIMs) have recently emerged as a promising architecture for large-scale beamforming systems, including cell-free massive MIMO (CF-mMIMO), due to their cost-effective wave-domain signal processing capabilities. However, existing algorithms for the joint optimization of digital and SIM-enabled wave-domain beamforming typically incur prohibitive computational complexity. In this work, we propose an efficient alternating optimization (AO) algorithm for weighted sum-rate maximization in SIM-assisted CF-mMIMO systems employing hybrid digital-wave beamforming. Unlike prior approaches that rely on general-purpose optimization solvers or per-element gradient ascent methods, the proposed algorithm updates the digital and wave-domain beamforming variables on a per-access point (AP) or per-SIM-layer basis, enabling closed-form updates at each step. Numerical results demonstrate that the proposed algorithm reduces the computational complexity by more than 99% compared to existing algorithms while achieving nearly identical sum-rate performance.

Semantic communication improves task effectiveness by transmitting task-relevant information. However, most existing schemes remain organized as task-specific, end-to-end pipelines, which are difficult to reuse across models, applications, and deployment environments. Against this background, we propose the Semantic Internet of Everything (SIoE), a composable service architecture that represents heterogeneous communication and artificial intelligence (AI) functions as capability-profiled services and coordinates them according to application objectives. SIoE comprises three planes: a task and service plane, an agentic orchestration plane, and a semantic capability plane. In this framework, task requirements are captured via a semantic service-level agreement (SLA), while an agentic planner discovers and composes candidate capabilities under deterministic compatibility, resource, privacy, and policy validation. Feedback from the communication, semantic, and task levels enables continuous adaptation and replanning. A lightweight vehicle-to-everything case study illustrates profile-grounded capability planning under explicit service constraints. The results demonstrate the feasibility of decoupling service objectives from fixed communication implementations and also highlight key open challenges, including semantic SLA design, capability interoperability, scalable planning, and trustworthy execution.

Autonomous underwater vehicles (AUVs) are increasingly important tools in industries ranging from research, to energy, to defense. AUVs are power-constrained platforms operating in remote environments with fixed battery capacities, where propulsion competes with compute and sensors for power over lengthy mission durations. AUVs frequently operate in dark or turbid waters where optical sensing is of limited value, and rely on sonar as their primary sensing modality. Convolutional neural networks (CNNs) are the state-of-the-art solution for object detection in forward-looking sonar imagery, but are energy expensive (e.g. YOLOv8m: 322 mJ/inference). Spiking neural networks (SNNs) rely on binary spike activations and thus sparse accumulate-only operations, allowing them to be remarkably energy efficient, particularly when paired with dedicated neuromorphic hardware. The sparse, high-contrast structure of forward-looking sonar (FLS) returns is structurally matched to spike coding in a way that optical imagery is not. No prior work has assessed the suitability of SNNs for object detection in FLS imagery. SpikeYOLO, a fully spiking network trained with surrogate gradients, was benchmarked against state-of-the-art CNN baselines on three FLS object detection datasets. Key results: SpikeYOLO T=2 achieves 3.3$\times$ lower theoretical compute energy on UATD (97 vs 322 mJ) at competitive accuracy (0.529 mAP@0.5:0.95 vs. YOLOv8m's 0.575); SpikeYOLO matches YOLOv8m on mAP@0.5 and outperforms YOLO-SONAR and Fast R-CNN baselines on the sparse Marine-Debris-FLS dataset at 4.4$\times$ lower energy; SpikeYOLO demonstrates superior robustness to multiplicative speckle noise (3.0% degradation at $\sigma{=}0.4$ vs. 8.9% for YOLOv8m), outperforming YOLOv8m outright at $\sigma{=}0.6$, directly relevant to real-world FLS deployment.

In sound mixtures of more than one musical source, different vibrato patterns act as a cue that multiple sources are present for both human listeners and source separation algorithms. Matching the vibrato patterns of the signals in the mixture reduces the perception of multiple sources, particularly when the sources play in unison. This work introduces the vibrato matching algorithm, which first suppresses vibrato in a target signal and then transfers vibrato from a source signal to the target. An existing vibrato suppression algorithm is combined with a new algorithm for vibrato transfer, which imparts frequency modulation and amplitude modulation to the harmonics of the target signal, and amplitude modulation onto the spectral envelope of the non-harmonic residual component. Examples demonstrate the algorithm's utility as a vibrato control mechanism and as a tool for blending sound sources. Matching vibrato degrades the performance of source separation algorithms, suggesting a similar degradation in listeners ability to detect the presence of multiple sources.

Indoor immersive communication, empowered by millimeter-wave (mmWave) technologies, is a key enabler for 6G-ready Extended Reality (XR) applications. However, mmWave signals are highly susceptible to dynamic obstacles (e.g., moving humans), leading to frequent line-of-sight (LoS) blockages. Moreover, although massive-antenna systems have been widely adopted indoors, their fixed-site deployment limits their effectiveness in wide and dynamic indoor environments such as factories and offices. To address these challenges, we present a sensing-assisted anti-blockage Pinching-Antenna Systems (PASS) solution for indoor immersive communications. By mechanically moving a pinching antenna (PA) along a dielectric waveguide, PASS can flexibly extend the service coverage and re-establish LoS links in wide indoor scenarios. Besides, our solution integrates sensing capabilities into PASS, where the PA executes controlled micro-movements to transmit and receive chirp radar signals, enabling real-time sensing of obstacles. Based on the sensing results, we design an anti-blockage PA movement strategy that detects blocked regions along the waveguide and proactively moves the PA to safe zones. Accordingly, the whole system performs a cycled "sensing-movement-communication" workflow with each cycle completed in a limited frame to ensure both timely sensing and communication. To ensure system efficiency, a closed-form pre-optimized solution of sensing parameters is derived under practical mechanical and sensing constraints. Extensive experiments show that our solution reduces blockage-induced outages to near-zero while sustaining the effective throughput required by indoor immersive applications.

Channel charting has emerged as a promising paradigm that maps high-dimensional channel state information into a low-dimensional latent space, facilitating tasks such as radio environment sensing and beam management. However, existing methods often rely on precise user locations or timestamp-based pseudo-labels, which are difficult to obtain in privacy-sensitive scenarios and rapidly varying wireless environments. To address these limitations, we propose propagation-aware channel charting with physics-guided metric learning (PACC), a location-free framework that constructs channel-domain supervision from propagation characteristics without requiring explicit geographic information. Specifically, PACC designs a propagation-aware dissimilarity metric that adapts to line-of-sight and non-line-of-sight propagation conditions, thereby preserving both local neighborhood relationships and the intrinsic geometry of the radio environment. Simulation results demonstrate that PACC consistently outperforms both classical dimensionality-reduction methods and state-of-the-art learning-based channel-charting approaches under diverse propagation conditions.

Semantic communication has emerged as a promising paradigm for next-generation wireless systems by transmitting high-level semantic features rather than raw bits. However, collaborative devices and multimodal transmission increase privacy risks because sensitive information may leak through inter-device semantic fusion and cross-modal representations. To address this issue, we propose a privacy-preserving semantic communication framework for wireless edge networks. Leveraging a vision-language model (VLM), the framework extracts textual semantics from images and identifies privacy-sensitive entities using a privacy database maintained only at the edge server. Before image transmission, each device removes the identified private regions while preserving useful semantic content. The server then reconstructs the removed regions from the received masked images using textual embeddings and VLM-based semantic priors. To protect textual information, we design an encrypted semantic-channel transceiver using physical-layer keys generated from reciprocal wireless channels, without pre-shared keys. We also introduce a semantic information bottleneck to suppress redundant information across multiple devices. The framework is evaluated against a strong model-aware adversary that can intercept wireless transmissions and access edge-device model parameters but not server-side data. Simulation results show that the proposed method reduces privacy leakage by more than 50% compared with a semantic communication scheme without privacy protection, while the authorized server achieves a 48% improvement in perceptual reconstruction quality over the adversary. The estimated mutual information between transmitted representations approaches 0 bit, indicating effective suppression of cross-device semantic redundancy.

Extremely large antenna arrays at high frequencies substantially extend the radiative near-field region in 6G networks, making mobile beam alignment depend jointly on user angle and range. The added range dimension enlarges the beam-search space, making repeated pilot-based sweeping costly under mobility, while sensing-assisted tracking depends on propagation conditions, echo quality, and target reflectivity. We propose an adaptive payload-aided near-field beam-tracking framework based on maximum likelihood estimation (MLE) and Thompson sampling (TS). Selected received payload samples are fed back and reused as tracking observations, avoiding dedicated beam-sweeping symbols during tracking. Within each sliding window, local angle and range trajectories are modeled by low-order polynomials to capture velocity, acceleration, and higher-order motion variations, and are estimated by MLE using the spherical-wave channel model. A local Gaussian approximation centered at the MLE, with covariance from the inverse observed Fisher information, represents trajectory uncertainty. For payload transmissions selected for feedback, TS samples a trajectory hypothesis and maps it to a payload beam toward the sampled state, while the remaining transmissions use the MLE-predicted beam. To handle nonstationary mobility, an asymptotic chi-square characterization of in-window estimation risk motivates joint adaptation of observation-window length and polynomial degree, while online residual statistics adjust the update interval and feedback ratio. The framework is also extended to uniform planar arrays with elevation tracking. Simulations under smooth and sharp-turn trajectories show high payload-accounted mean normalized beamforming gain, low normalized-gain variance, and high effective-symbol reliability, while the adaptive mechanism provides substantial robustness under nonstationary sharp-turn mobility.

Diffusion models are widely used as priors for linear inverse problems, yet endpoint quality does not reveal when measurement information enters reverse denoising or how it is allocated across signal directions. We study this process through the smoothed likelihood force, the difference between exact posterior and prior scores at each noise level. For a fixed measurement, its expected squared norm gives both posterior–prior relative-entropy dissipation and reverse-path relative-entropy growth. Averaging over measurements yields an information–minimum mean-square error (I-MMSE) identity linking information gain to denoising-error reduction. Under finite second moments, the force energy and its ratio to prior-score energy decay quadratically in the noising kernel's signal coefficient at high noise. Solvable models show that conditioning removes class separation already explained by the measurement, reduces a uniform index entropy over \(n\) empirical samples from \(\log n\) to \(H(I\mid r)\), and makes assimilation depend on operator–prior alignment even for identical singular values. Experiments in models with tractable posteriors evaluate these predictions. In a separate illustration with a frozen FFHQ model, masks sharing the same spectrum yield different prior-normalized null-space trajectory statistics.