Papers

This paper presents a low-complexity precoded MIMO-OFDM system for achieving improved spectral efficiency (SE) via intentionally compressing information symbols among subcarriers. Particularly, the proposed scheme leverages the powerful random multiplexing mechanism for precoding, and adopts the linear-complexity orthogonal approximate message passing (OAMP) estimator for symbol detection, where the compatibility with the existing fifth generation (5G) architectures is fully preserved. We further provide the theoretical analysis based on the replica-symmetric (RS) formula. This analysis confirms the advantages of the proposed system with respect to the adopted compression ratios, where an interesting phase transition behavior is verified. Numerical results coincide with our analysis and demonstrate significant improvements in terms of achievable rates and bit error rate (BER) compared to conventional MIMO-OFDM counterpart, making the proposed scheme a promising solution to 6G and beyond wireless networks.

Singular value decomposition (SVD) is a core operation in multiple-input multiple-output (MIMO) beamforming, but the cubic complexity of standard SVD routines can lead to a major latency bottleneck as array dimensions scale to extremely large sizes. This paper presents a fully learned neural operator that avoids explicit SVD computation by directly mapping channel matrices to truncated low-rank factors for precoder and combiner design. In contrast to iterative numerical solvers and algorithm-unrolled networks, the proposed structure-aware model, termed SVDNet, produces these factors in a single forward pass at inference, shifting the per-instance decomposition cost to offline training. The model also includes lightweight constraints to enforce basic algebraic properties required by beamforming, such as semi-unitarity of the singular vectors and nonnegative singular values, without invoking matrix factorization kernels. Experiments on extremely large-scale MIMO channels with matrix dimensions up to 512*512 show that the proposed approach achieves spectral efficiency close to exact SVD-based beamforming in single-stream transmission and consistently improves multi-stream sum-rate over representative learned baselines, indicating good scalability for low-latency wireless processing.

Complex SAR imagery is usually visualized and evaluated mainly through its magnitude. Phase is retained in the complex data but is rarely treated as a direct image-quality objective. Existing sparse reconstruction methods typically focus on magnitude fidelity and single-epoch complex reconstruction accuracy. However, the phase difference between two acquisitions is what drives line-of-sight deformation retrieval in InSAR, from ground subsidence monitoring to earthquake deformation mapping. This paper proposes the Differential-Phase-Oriented Joint Masked Reconstruction Network (DP-JMRNet), which uses deep unfolding to reconstruct the two epochs jointly from masked observations under a differential-phase objective. An exchange-equivariant interaction module makes the reconstruction independent of epoch ordering. A coherence-aware gate opens cross-epoch sharing in coherent regions and closes it where the two epochs disagree. On simulated bitemporal SAR data, DP-JMRNet attains the lowest differential-phase RMSE at 30%, 40%, and 50% sampling rate, while maintaining competitive amplitude and complex-image fidelity. This corresponds to a 47.5%–51.3% reduction over the best baseline, achieved with one third of its parameters. The same trend is validated on three Sentinel-1 scenes. A systematic study of acquisition design further shows that sharing the same aperture support across epochs is necessary for phase fidelity, whereas optimizing the sampling mask does not improve the differential phase. The code and data are available at https://github.com/JasonBao05/coherent-sar-unfolding.

The aged population is exposed to risk situations, like falls, sudden changes in vital signs, and faints. These situations become common at this life stage due to a decrease in body capacity and illnesses increase. This scenario imposes attention on families and the national health system. Some studies propose to overcome this problem through elderly health monitoring systems based on smartphones, smartwatches, and other equipment that usually cover low-efficient communication systems and low battery life. This paper proposes an efficient health monitoring system based on Sigfox IoT service and a watch-type wearable device for sensing elderly motion, location, and vital signs to predict risk situations designed to achieve at least 30-days of battery life. Our experimental results show that this architecture is a low-cost, low-power, efficient, and feasible elderly health monitoring solution.

Automated sleep stage classification based on single-channel EEG is a promising pathway to large-scale sleep monitoring outside the polysomnographic lab. This paper proposes SleepEffFormer TAS, an efficient and interpretable system that consists of a four-stride-block 1D CNN feature extractor, a two-layer pre-normalization Transformer encoder, and a non-parametric Transition-Aware Smoothing (TAS) layer that suppresses physiologically unrealistic transitions between predicted stages. When tested on the Sleep-EDF Expanded dataset using 78 all-night EEG recordings from the Fpz-Cz channel with a subject-wise held-out split, the proposed model achieves 83.9% accuracy, a 78.9% macro F1-score, and a Cohen's kappa of 0.765 with approximately 367K learnable parameters. This performance is comparable to AttnSleep while using 3-5 times fewer parameters. Ablation studies show that the Transformer encoder improves macro F1 by 6.6 percentage points over a CNN-based system, while the TAS layer provides an additional 1.8 percentage point improvement without introducing any trainable parameters. Weighted loss is also critical for improving classification of the minority N1 sleep stage. Attention maps generated by the proposed model reveal physiologically sensible sleep-stage-related EEG features.

Freezing of gait (FoG) is one of the most debilitating episodic motor symptoms of Parkinson's disease (PD). FoG detection from wrist worn inertial signals poses challenging problems such as extreme data class imbalance (only 12-18% of signal windows are FoG), low frequency postural instabilities alongside diagnostically crucial 3-8 Hz rhythmic tremors, and the need for subject independent generalisation. To address these, this paper presents WaveFoG, a novel wavelet gated Transformer architecture that combines multi scale representations from discrete wavelet transform (DWT) with a dual branch encoder. A 1D CNN captures local gait texture, and a Transformer captures global temporal context. They are fused via a sigmoid gating fusion layer that modulates the encoder output based on the DWT sub-band descriptors. Trained with focal loss and evaluated on the public Kaggle TLVMC FoG Prediction dataset under subject independent grouped ten fold cross validation (SI-CV), WaveFoG achieves a mean F1-score of 0.875 +/- 0.017 and AUPRC of 0.833 +/- 0.020, exceeding single branch baselines (1D CNN, Bi-LSTM, and vanilla Transformer) by up to 5.4 pp on F1. Temporal saliency maps highlight temporal patterns that are consistent with reported characteristics of FoG onset. The source code of this project is available at: https://github.com/Shihabul-Shuvo/WaveFoG

In this work, we derive the Cramér-Rao bound for joint Delay-doppler estimation with multi-carrier waveforms

A joint communication and control (JCC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users (CUs) and controls a physical plant in a closed loop. In the downlink, BS-generated control inputs are transmitted to and recovered at the plant, with wireless actuation distortion incorporated into the plant-state evolution. In the uplink, the plant state is reported to the BS and tracked by a Kalman filter (KF) for subsequent control-input generation. To characterize long-term control performance under communication-control interference, finite- and infinite-horizon linear quadratic Gaussian (LQG) costs are derived, directly linking beamforming design to plant-state evolution. JCC beamforming problems are then formulated for vector- and scalar-valued control inputs to minimize the infinite-horizon LQG cost subject to per-user communication signal-to-interference-plus-noise ratio (SINR) requirements. For the vector case, a second-order cone programming (SOCP)-based successive convex approximation method is developed for the resulting nonconvex problem. For the scalar case, a closed-form infinite-horizon LQG cost is derived, and the communication-control Pareto boundary is optimally characterized by an SOCP-based bisection method. Its optimality follows from the strict monotonicity of the scalar control cost with respect to the control SINR. Numerical results show that the derived costs closely match Monte Carlo simulations, the KF accurately tracks the ground-truth plant-state trajectory, and the proposed methods consistently outperform the zero-forcing benchmark. This confirms the benefit of balancing communication-control interference, especially with limited spatial degrees of freedom (DoFs).

We propose a novel framework for decomposing nonstationary spiky signals. Unlike classical adaptive non-harmonic models, which represent signals as amplitude- and frequency-modulated (AM–FM) oscillations, the proposed model is designed for signals whose dominant structures are highly localized, impulsive, or spike-like, and contains physiological variability, which is challenging to be modeled as AM–FM representations. Representative examples include electrocardiogram (ECG) complexes, epileptic electroencephalogram (EEG) transients, and other pulse-like physiological signals. We first introduce a fixed-waveform model for repetitive spiky structures and then extend it to accommodate cycle-to-cycle waveform variability. The resulting algorithm, termed Spiky Shape-adaptive Mode Decomposition (SSAMD), estimates waveforms in the Fourier coefficient domain. Fourier coefficients are first estimated via local regression and then regularized by exploiting their low-dimensional manifold structure. Specifically, we combine a PCA-based parametrization with an entropy regularization acting on the singular-value spectrum of the coefficient matrix, promoting morphological consistency while preserving structured waveform variability. The proposed framework is flexible and overcomes the limitations of existing models. We validate the method on synthetic and real biomedical signals, including ECG with atrial fibrillation, epileptic EEG, and trans-abdominal maternal ECG. Experimental results demonstrate effective denoising, waveform tracking, decomposition, and segmentation, while preserving physiologically meaningful morphological variations.

We provide an overview of the problem of jointly testing two hypotheses and estimating a parameter of the selected model. Such problems arise in a variety of applications. First, we present a conceptual introduction to suboptimal and optimal procedures for joint detection and estimation. A numerical example illustrates the advantages of the optimal procedure over suboptimal ones. Next, we discuss how more advanced problem formulations affect the presented results. The second part covers joint detection and estimation in a sequential framework. First, we provide an introduction to sequential analysis through sequential hypothesis testing. Then, suboptimal and optimal sequential procedures for joint detection and estimation are discussed. A numerical example shows the advantages of optimal sequential procedures over suboptimal and optimal sequential procedures with a fixed number of samples. The third part discusses open problems and future research directions in joint detection and estimation.

Decentralized wireless collectives including vehicular swarms, IoT clusters, and edge AI networks require communication protocols that maintain robustness under dynamic topologies and heterogeneous link quality. While Random Linear Network Coding (RLNC) provides algebraic resilience against packet erasures, its performance degrades significantly when peers exhibit diverse channel conditions. This paper presents Adaptive Peer Clustering with Hierarchical RLNC (APC-RLNC), a system that dynamically groups peers by exponentially weighted moving average (EWMA) reliability metrics and applies multi-tier network coding within and across clusters. We formalize the clustering optimization problem, derive closed-form decoding probability bounds for Markov erasure channels, and prove O(sqrt(T)) regret for online reconfiguration under the Follow-the-Regularized-Leader (FTRL) framework. Our implementation includes both a high-fidelity network simulator and a proof-of-concept testbed deployment on Jetson Nano edge devices. Evaluation across diverse scenarios including high-mobility vehicular networks, burst-error channels, and adversarial interference demonstrates 5.2-9.8 percentage-point packet delivery ratio (PDR) improvements, 10-23% latency reductions, and up to 30% higher node retention compared to state-of-the-art baselines. The system exhibits linear scalability to 500+ nodes and maintains real-time reconfiguration overhead below 3%. APC-RLNC establishes adaptive clustering as a foundational primitive for AI-native 6G wireless systems.

Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fusion strategies that assume both modalities are equally trustworthy can become less reliable than a single clean modality under degradation. We present CardioFusion-AI, a framework whose signal-processing front end, including R-peak and systolic-peak detection, an Orphanidou-type signal-quality index, and beat-by-beat pulse transit time estimation, is validated on 53 real intensive-care recordings (848 windows; heart-rate mean absolute error 1.61 bpm for ECG and 2.78 bpm for PPG) and a real annotated fetal ECG database (R-peak F1 0.89-0.98). We then conduct a controlled synthetic degradation study comparing eight ECG-PPG fusion strategies across six degradation regimes spanning graded corruption and complete modality loss, using five independent training seeds. Attention fusion achieved the lowest descriptive overall error (1.66+/-0.43 bpm). Both adaptive gates reallocated weight toward the healthy modality under complete modality loss, but showed near-zero correlation between gate weight and signal quality under graded degradation (r = 0.10-0.24). Signal-quality conditioning produced a specific improvement under missing-PPG conditions (1.56+/-0.59 bpm), approaching the 1.48 bpm unimodal ceiling. With only five training seeds, no pairwise comparison survives Holm-corrected significance testing; effect sizes and confidence intervals are therefore reported. These results indicate that modality availability and modality quality are functionally distinct problems for adaptive fusion.

Wireless multipath propagation, beamforming, and polarization are central concepts in radio systems, but they are difficult to observe directly because radio-frequency fields are invisible to humans. This paper presents an augmented-reality visualization system that turns phase-coherent WiFi channel measurements from the ESPARGOS antenna array into a live camera overlay. The system estimates a two-dimensional beamspace representation, registers it with the optical camera view, and overlays the resulting angular spectrum onto the physical scene. Additional visual layers show relative path delay and polarization, the latter computed using Jones calculus from channel measurements captured from two separate antenna feeds. The result is an intuitive “WiFi camera” for science communication, teaching, and experimental debugging, while all displayed quantities remain directly derived from measured channel data.

This paper proposes a maximum-likelihood (ML) framework for estimating nonlinear frequency-selective channels in orthogonal frequency-division multiplexing (OFDM) communication systems. The nonlinear distortions are modeled using a Discrete Cosine Transform (DCT)-based representation, which results in a well-conditioned estimation problem with favorable convergence properties. The proposed method combines a compact parameterization with low computational complexity, enabling fast adaptation and efficient real-time implementation. Numerical results show that the proposed channel estimation can be used for multiple nonlinear compensation methods and achieve near-ideal BER performance with very limited training overhead. This performance is maintained down to approximately 15 dB SNR in the presence of both amplitude and phase nonlinearities, and down to -10 dB when only amplitude distortions are present.

Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics or artifacts of data collection remains unresolved. We evaluated the cross-dataset generalizability of classical ML and deep learning (DL) cough-based TB classifiers across three independent datasets. Despite moderate within-dataset performance (ROC-AUC up to $0.755 \pm 0.056$), both pipelines fail to generalize, with external performance frequently below 0.6, indicating a possible limitation of the data. We further observed audio representations are organized by recording device and dataset rather than TB status, predicted TB probability tracks country-level prevalence in CODA, and device mismatch degrades transfer while device-diverse training improves it. Additionally, a clinical-variable baseline generalizes more consistently (ROC-AUC $0.655 - 0.711$), indicating acquisition-specific variability is a stronger driver of poor generalizability than population shift. High within-dataset performance is not enough. External validation is essential before cough-based TB models are clinically ready.

Coupled feedback networks are often monitored channel by channel even though cross-channel paths alter both stability margins and transmitted disturbances. We study identification of a structured feedback matrix L_t = Phi diag(gamma_t) in an output-only setting: no commanded, probing, or reference input exists – only temporally separated outputs and the scheduling gains gamma_t are observed, while the coupling response Phi and the clearing-window inputs are not. Identification rests jointly on the persistent excitation of the observed pre-window output and on two structural features separating coupling from confounds: the known time variation of the gains, which modulates the closed-loop response in a predictable pattern, and a partial-reversal moment by which a known fraction of transient displacement is corrected in a subsequent window. We give a hierarchy of results: exact local identification of the coupling under a Jacobian rank condition on the gain regimes; a first-order interaction estimator whose identification strength is the minimum eigenvalue of the residualized interaction information matrix (provably unidentified under constant gains); and a characterization of the estimand as a resolvent sensitivity – the right object for screening transmitted disturbances and a first-stage input to spectral-margin recovery – with sqrt(T) asymptotics for the first-order estimator, a cross-identification theorem mapping each varying gain to exactly identified resolvent rows and columns, and bootstrap validity under consistent selection; the implemented heuristic's empirical coverage (90% at nominal 95%) quantifies the remaining gap. Simulations verify sharpness of the rank condition and quantify benchmark failures under confounding. A case study on leveraged-fund rebalancing feedback, where daily fund disclosures play the role of the known gains, illustrates the method on real data.

Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy. However, FL struggles with heterogeneous, non-IID client data distributions, resulting in sub-optimal and biased global models. In this paper, we propose pFedMARL, a novel approach leveraging Multi-Agent Reinforcement Learning (MARL) with Twin Delayed Deep Deterministic Policy Gradient (TD3) to dynamically adapt aggregation strategies in FL settings. Our method employs a server-side agent adjusting client contributions to optimize global model robustness and client-side agents balancing global and local updates to personalize models effectively without pre-training. We demonstrate superior performance of pFedMARL for training a semi-supervised audio spectrogram transformer, matching or outperforming FedAvg, Ditto, and local training approaches across multiple non-IID scenarios and in the presence of adversarial clients. Our results indicate that pFedMARL actively improves accuracy, robustness, and fairness, making it suitable for real-world deployments.

Semi-blind channel estimation offers an attractive tradeoff between pilot overhead and estimation accuracy in large-scale wireless systems. However, reliable channel acquisition becomes particularly challenging in highly dynamic environments such as non-terrestrial networks (NTNs), where rapidly varying channels and high system dimensionality significantly degrade the performance of conventional covariance-based estimators due to sampling noise. In this paper, we propose a robust semi-blind channel estimation framework for multi-user uplink systems operating in NTN systems. The proposed approach introduces an optimally regularized least-squares formulation that balances training-based information and blind subspace structure. By exploiting the spiked covariance model within a random matrix theory (RMT) framework, we derive a closed-form characterization of the resulting channel mean-squared error and obtain an analytically tractable design of the optimal regularization parameter. The resulting estimator is computationally efficient and particularly well suited to high-dimensional regimes. Simulation results under realistic Third Generation Partnership Project (3GPP) NTN channel models demonstrate substantial performance improvements over conventional semi-blind and training-based estimators.

The ultra-high data rates enabled by terahertz (THz) communications pave the way for the demanding requirements of industrial Internet of Things (IIoT) applications, making the investigation of THz channels in industrial environments a critical research topic. This paper presents a comprehensive statistical analysis of the propagation channel at 318\,GHz in an industrial environment. In particular, a new clustering scheme is proposed for the sparsity observed in the multipath components (MPCs) of the measured channel. Furthermore, statistical analyses are conducted separately for the group of strong reflections, defined as primary clusters, and other propagation phenomena, defined as random clusters, in a rich-scattering environment. The results demonstrate that the large-scale parameters are predominantly influenced by these strong reflections. This study provides reliable support and guidance for subsequent THz stochastic channel modeling.

Wi-Fi sensing has emerged as a promising modality for contact-free respiratory monitoring in home healthcare due to its ubiquity. However, conventional approaches typically treat Channel State Information (CSI) subcarriers uniformly, neglecting their heterogeneous responses to breathing motions. To address this limitation, we propose a subcarrier-aware sensing framework that characterizes subcarrier-dependent respiratory sensitivities and exploits them for informative subcarrier selection. Based on the statistical properties of breathing-sensitive subcarriers, we further develop an unsupervised clustering-based breathing estimation method for robust respiratory monitoring. Experimental results show that the proposed method achieves 97% breathing rate estimation accuracy and reduces the mean absolute error (MAE) by 0.45 breaths per minute (bpm) compared with baseline methods. Furthermore, we extend the analysis to breathing-pause detection, which derives an amplitude-attenuation-based threshold mechanism. Under breathing pauses, the proposed method achieves an MAE of 1.6 bpm in breathing rate estimation. These results demonstrate the robustness and effectiveness of the proposed framework across different scenarios and its potential for contactless home healthcare monitoring.

Networked sensing, which jointly exploits observations from multiple distributed nodes, is essential for unlocking the full sensing potential of integrated sensing and communications (ISAC). This article introduces multi-UE sensing, a new networked sensing paradigm for future perceptive mobile networks that exploits the correlated sensing observations naturally arising from distributed user equipment devices (UEs) interacting with common targets. Representative uplink, downlink, and hybrid sensing architectures are presented, together with a multi-view signal processing framework encompassing synchronization, correlation-aware parameter estimation, and sensing fusion. Key open challenges, including correlation modelling, target association, sensing information compression, and communication-sensing co-optimization, are also discussed.

Satellite-enabled wireless power transfer (WPT) may be a transformative solution for charging Internet of Things (IoT) devices in off-grid scenarios where traditional technologies struggle to efficiently meet urgent energy demands. In this article, we review the advantages and limitations of microwave-based long-distance charging for satellite-enabled WPT. We then introduce our vision of coordinated space-based WPT, where multiple satellites jointly serve networks of ground devices. Potential use cases are presented highlighting application requirements. We evaluate the average received power at the target locations using two coordination schemes and perform a statistical characterization of the power spillover on undesired locations. We also shed light on the performance of inter-satellite laser WPT for different operating distances and transmit-receive apertures of the peer satellites. Moreover, we explore the integration of metasurfaces on satellite apertures and ground networks to boost energy conversion efficiency, scalability, and beam management. Finally, we outline relevant challenges and research directions towards implementing our vision.

This paper investigates a near-field (NF) multiple-input multiple-output (MIMO) communication system equipped with dual uniform planar arrays (UPAs). We first develop a generalized geometric model to calculate the 3D distance between arbitrary antenna elements across the transmitter and receiver panels. Leveraging the distance analysis, we derive a closed-form near-field to far-field (NF-FF) boundary for dual-UPA configurations. By exploiting the geometric structure of the UPAs, we further decompose the near-field channel matrix into a Kronecker-product of two lower-dimensional matrices. This decomposition enables a low-complexity NF beamforming design for achievable-rate maximization. Numerical results validate the analysis and demonstrate that the conventional Rayleigh distance is a special case of the generalized model. Furthermore, the proposed beamforming design achieves near-optimal rate performance while significantly reducing the computational complexity compared to state-of-the-art NF beamforming methods.

Reliable beam management is a central challenge for 6G millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) links, where narrow beams provide high array gain but are vulnerable to mobility-induced misalignment, blockage, and domain variation. BeamGuard is a multimodal sensing-aided beam-management framework that combines exteroceptive sensing with optional partial in-band mmWave power observations to forecast future beam distributions and select adaptive virtual beamwidth actions for reliable V2I control. It fuses camera, radar, LiDAR, GPS, and mmWave power observations with a temporal multimodal forecaster, then converts the predicted posterior into a beam center and virtual codebook-level beamwidth through a risk-aware planner. Here, virtual beamwidth denotes adjacent-beam coverage in the codebook index space rather than physical analog wide-beam synthesis. BeamGuard supports sensor-only operation for beam-training overhead reduction, limited in-band operation with masked beam-power entries, and full hybrid operation with sensing and communication-side measurements. We evaluate BeamGuard on DeepSense 6G Scenarios 32 and 33, with additional held-out tests on Scenarios 31 and 34, covering day–night training, transfer, limited adaptation, ablations, budget sweeps, and lightweight baselines. The full-hybrid anchor, used as the complete-system reference, achieves Top-1/Top-3/Top-5 accuracies of approximately \(0.393/0.778/0.897\), while the planner attains a threshold-based outage probability of about \(0.0060\) with a gain ratio of about \(0.895\). Matched-budget baselines further show that BeamGuard improves over multilayer perceptron, recurrent, and temporal convolutional predictors under comparable in-band observation settings. These results demonstrate robust, overhead-aware beam management through multimodal forecasting and risk-aware virtual beamwidth control.

Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client–server collaborative inference in resource-constrained edge systems. However, directly transmitting token embeddings presents two practical challenges: substantial communication cost and limited interoperability across model-specific token embedding spaces. To address these challenges, we propose a token-oriented semantic communication framework. In this framework, token-level task relevance determines which compressed image latents are transmitted, enabling token-granular transmission without directly transmitting token embeddings. The framework is modular, coordinating three pretrained components—a lightweight client-side vision transformer (ViT), a learned image compression (LIC) model, and a large server-side ViT—without end-to-end training. The key enabler is the one-to-one spatial alignment between ViT patch tokens and the LIC latent vectors, which allows token-level task relevance to directly determine which latent vectors are transmitted. Building on this alignment, token-aligned LIC selectively transmits task-relevant latents, layer-selective attention rollout estimates token relevance from a selected range of attention layers in a single forward pass, and surrogate token substitution adapts the frozen server model by optimizing a single learnable token. Experiments on ImageNet show that the proposed framework achieves a more favorable rate–accuracy trade-off than recent semantic communication schemes, hand-crafted codecs, and task-agnostic LIC models.

Millimeter-wave (mmWave) multi-user MIMO systems are highly susceptible to dynamic blockages, and reconfigurable intelligent surfaces (RIS) have been introduced as a remedy. However, RIS links can themselves be blocked, while existing studies often assume ideal availability. This paper proposes an end-to-end mobility-aware multi-RIS optimization framework that integrates per-RIS blockage detection with closed-form Riemannian updates. The base station transmits short indexed synchronization signals, enabling each user to identify blocked panels via a simple energy test. Based on the detected feasible sets, we jointly optimize the BS precoder and RIS phases using a Stochastic Closed-form Riemannian Phase Alignment (SCRPA) algorithm, which ensures unit-modulus feasibility, monotone convergence, and low complexity. Extensive simulations validate reliable blockage detection and demonstrate significant weighted sum-rate and scalability gains compared to existing baselines.

Frequency-to-time mapping (FTTM)-based photonics-assisted spectrum sensing enables wideband spectrum monitoring for applications such as cognitive radio and electronic warfare. However, FTTM-based photonics-assisted spectrum sensing approaches involve trade-offs among analysis bandwidth, temporal resolution, and frequency resolution. Although channelization has been introduced to alleviate these trade-offs, the limited number of available channels in previously reported systems constrains further improvements in overall performance. Here, we propose a photonics-assisted dual-comb channelization architecture for broadband frequency measurement and time-frequency analysis. The system employs a stepped-frequency pump based on stimulated Brillouin scattering (SBS) to generate an optical filter comb and adopts a single-laser design for the pump, probe, and reference paths to enhance frequency stability while reducing system complexity. Experimental results show a 48-GHz analysis bandwidth in the 16-channel configuration and 52 GHz in the 20-channel configuration, together with microsecond-scale temporal resolution and a frequency resolution better than 80 MHz. The proposed architecture therefore offers a practical and reconfigurable solution for real-time wideband spectrum sensing.

This paper investigates heterogeneous secure multi-user transmission in pinching-antenna systems (PASS), where dynamically adjustable pinching antennas reshape both guided-wave and free-space propagation to improve communication and confidentiality performance. Unlike conventional physical-layer security designs that represent different security requirements merely through weights or thresholds, heterogeneous services may change the logical role of each receiver for each information stream. To address this issue, we establish a unified role-dependent PASS transmission framework comprising low-, medium-, and high-security modes. These modes respectively maximize the minimum legitimate-user rate, protect confidential streams against external eavesdroppers, and further prevent non-target legitimate users from intercepting unauthorized information. The resulting joint optimization of information beamforming, artificial noise, and pinching-antenna positions is formulated as a long-horizon continuous-control problem. Two learning-based controllers are then developed to provide complementary complexity-performance tradeoffs. First, heterogeneous security-aware proximal policy optimization (HSPPO) directly transforms mode-specific rate and secrecy violations into normalized smooth feedback embedded in the proximal-policy-optimization advantage, enabling lightweight and violation-sensitive control. Second, multi-relational hierarchy-aware diffusion policy optimization (MRHA-DPO) combines a PASS-aware multi-relational graph encoder, a graph-conditioned hierarchical velocity network, and exact-inversion DPO training to achieve topology-aware and expressive control. The proposed framework enables a common PASS platform to flexibly support service-dependent confidentiality requirements while balancing online efficiency and control capability.

Social dynamical networks significantly influence contemporary digital landscapes, affecting realms from social activism to public policy formulation. This paper investigates the use of multi-agent systems (MAS) to monitor and analyze these networks. Firstly, we propose a single-time-scale distributed inference model designed to effectively manage challenges such as latency and agent failure. Secondly, we provide sufficient conditions that ensure the stability of the proposed scheme. Notably, the observer gain design remains effective regardless of time delays. Thirdly, we develop a computationally efficient recovery mechanism for agent failures that relies on employing graph-theoretic approaches to restore network observability by replacing failed agents (due to sensing failure or unbounded delays, i.e., packet drops) by implementing computationally efficient graph-theoretic methods to assign observationally equivalent agent counterparts. Lastly, we illustrate the proposed scheme through a pedagogical example and real-world network applications.

Radio maps and channel knowledge maps provide reusable propagation knowledge, but a stored map can become locally outdated after persistent changes to doors, partitions, furniture, large equipment, or infrastructure. Motivated by digital-twin state synchronization, we develop an electromagnetic twin that recurrently converts sparse channel measurements into a persistent wireless state, exposes that state to communication queries, and uses its uncertainty to request subsequent measurements. The twin tracks persistent or semi-persistent propagation changes rather than transient human motion or fast fading. Each update is a constrained inverse problem that combines the previous map, a scene graph, and an imperfect physics prior. Measurement consistency with a confidence-calibrated radius and physical gain bounds enforce feasibility, while scene-aware spatial regularization and selective temporal memory preserve propagation boundaries and unchanged regions. Successive convex approximation (SCA), majorization–minimization alternating direction method of multipliers (MM-ADMM), and a low-complexity primal–dual hybrid gradient (LC-PDHG) mode realize the framework at different computational scales, and a local perturbation analysis gives an explicit multi-update tracking recursion. The updated state supports access-point association and codebook beam selection, while change-weighted A-optimal design closes the measurement–update–query loop. Over 500 realizations, the recurrent update remains stable through eight persistent scene events and raises best-beam accuracy from $76.64\%$ to $90.83\%$ as measured-location density grows from $2\%$ to $16\%$.