Papers

The development of the low-altitude economy has driven innovation in intelligent antenna systems within ISAC systems. In this paper, we investigate a Rotatable Antenna (RA)-enabled low-altitude integrated sensing and communication (ISAC) system. In practical terms, the RA array can flexibly adjust the three-dimensional (3D) beam direction of each antenna to enhance array directional gain, thereby improving the communication security of legitimate mobile users against potential eavesdropping risks from the unmanned aerial vehicle (UAV). Our objective is to maximize the minimum secrecy rate (SR) by jointly optimizing transmit beamforming matrix, transmit and receive RAs' pointing matrices. To this end, an multi-agent proximal policy optimization with three improvement mechanisms (MAPPO-T) algorithm is proposed to cope with the issue of complex multi-agent collaborative decision-making problem. Simulation results show that the introduction of RAs can effectively improve SR performance compared to the traditional fixed orientation antenna (FOA)-based system. In addition, the proposed MAPPO-T algorithm validate the superiority compared to the standard MAPPO algorithm.

We investigate the correlation between interleaver length and turbulence for a deployed terrestrial free-space optical link spanning 4.6 km. Using 24 hours of measurements, we show an indicative distribution of interleaver length for capacities up to 350 Gb/s/pol and outage probabilities down to $10^{-4}$.

In this paper, the Doppler frequency shift (DFS) is exploited as the only sensing parameter for low-cost indoor mobile device tracking. The existing trajectory tracking methods via DFS of Wi-Fi systems often require the knowledge of the starting position or additional information, like angle-of-arrival (AoA) and time-of-flight (ToF), to recover the trajectory of a moving target. This paper proposes the DoDTrack, a novel Difference-of-Doppler (DoD)-based tracking system, to track an active mobile device using a single receiver with distributed antennas. By comparing the signals received at the distributed receive antennas, which share the oscillator, the DoDs among the antennas can be detected robustly. Then, the reconstruction of the trajectory without prior knowledge of the trajectory starting position can be formulated as a minimum mean square error (MMSE) problem, which can be solved via alternating optimization. Particularly, the starting position and the trajectory shape are updated alternately in the proposed algorithm. In performance validation, we implemented the proposed DoDTrack design on a USRP-X310 platform, and assessed its estimation accuracy with various trajectory shapes in an indoor environment. Experimental results demonstrate that DoDTrack achieves a median tracking error of 0.34 m within a 6 m $\times$ 6 m sensing area, offering a high-precision and low-cost solution for active device tracking.

This paper presents a comprehensive performance comparison of several coded sequences for distributed acoustic sensing systems through numerical simulations. Correlation analysis reveals that perfect autocorrelation sequences provide accurate channel estimation. Phase noise sensitivity varies significantly with frequency diversity, with high-diversity sequences maintaining lower estimation errors for phase and intensity despite increased laser linewidths. For a coded distributed acoustic sensing system with \qty{10}{\hertz} laser linewidth, high-diversity sequences achieve $2.7\times10^{-3}$ rad phase error compared to $5.3\times10^{-3}$ rad for low-diversity codes for a \qty{1.5}{\kilo\meter} fibre length. The findings establish performance trade-offs between correlation quality, phase noise resilience, and practical implementation constraints for next-generation fibre-optic distributed acoustic sensing systems.

Background: Closed-loop neuromodulation integrating electroencephalography (EEG) and transcranial electrical stimulation (tES) has strong potential for neuroscience research and clinical applications. However, existing platforms often rely on benchtop instrumentation or FPGA-centered architectures, limiting portability and increasing system complexity. Methods: We developed a compact wearable bidirectional platform integrating 8-channel EEG acquisition and 2-channel tES within a single microcontroller unit (MCU). An ADS1299-based front-end supports up to 8 kHz sampling per channel for simultaneous neural and stimulation signal recording. The stimulation module uses direct digital synthesis (DDS) technique to generate programmable tDCS, tACS, and tTIS waveforms, with optimized firmware design enabling real-time operation on the microcontroller. Results: Experimental evaluation demonstrated high signal fidelity, with EEG correlation coefficients reaching $99\%$ under controlled conditions and $93.5\%$ on a gelatine head phantom. Stimulation performance showed current errors below $1\%$ across tDCS, tACS, and tTIS modes. The platform also reliably recorded concurrent EEG–tES signals without saturation during high-amplitude and high-frequency stimulation. Conclusion: These findings demonstrate that an MCU-centered architecture can effectively support simultaneous EEG sensing and multimodal tES delivery in a compact wearable form factor, while maintaining accurate stimulation and robust signal acquisition. Significance: This work provides a practical foundation for portable closed-loop neuromodulation systems with integrated host software, expanding access to personalized stimulation paradigms, and future point-of-care neurotechnology applications.

In this paper, we propose a tracking-assisted robust secure transmission framework for cell-free integrated sensing and communication (ISAC) networks that exploits distributed multistatic sensing to recursively track a mobile eavesdropper and quantify the associated position uncertainty. Since robust beamforming requires communication channel uncertainty rather than position uncertainty, directly incorporating tracking information into secure transmission is nontrivial. This challenge becomes more pronounced in cell-free ISAC, where the common position uncertainty propagates differently to the channels of geographically distributed access points (APs), resulting in coupled AP-specific channel uncertainties. To address this challenge, we fuse multistatic sensing measurements from distributed APs and sensing receivers using an extended Kalman filter (EKF) and propose a Jacobian-based anisotropic ellipsoidal uncertainty model that maps the EKF position-error covariance to coupled AP-specific channel uncertainties. Based on the proposed model, we formulate a robust sum secrecy-rate maximization problem under per-AP transmit-power and sensing mean-square error constraints and develop an efficient alternating optimization algorithm for the joint design of communication beamforming and sensing signals. Simulation results demonstrate a fundamental trade-off between tracking accuracy and secrecy performance, the benefits of distributed multistatic sensing and cooperative transmission in cell-free ISAC, and the effectiveness of the proposed robust design in improving secrecy reliability under mobility-induced channel uncertainty.

This paper proposes a novel ultra-low profile transmitted metasurface to generate enhanced non-diffractive orbital angular momentum (OAM) beams, employing a sparse feed array (SFA) to create a quasi-plane wave excitation for the first time. Our simulation indicates that with uniform amplitude excitation, the non-diffraction performance of Bessel beam produced by metasurface, surpasses that of conventional single-feed excitation. Based on this principle, a 5 * 5 sparse feed array is introduced and positioned less than one wavelength from the metasurface, ensuring a quasi-uniform amplitude excitation across all units. Further, this metasurface leverages its flexible phase control capability and integrates the spatial phase, OAM phase, and axicon phase to generate an enhanced high-order Bessel beam. The simulated results confirm a successful non-diffractive Bessel beam generation carrying OAM with mode l = +2, exhibiting reduced beam divergence and higher gain. This design also offers benefits of ultra-low profile, high aperture efficiency, low structural complexity.

The rapid growth of AI-driven data centers is introducing load behaviors that are not explicitly represented in conventional composite load models. This paper presents DC-CLM, a workload-aware extension of the WECC composite load model that incorporates UPS-supported IT demand, mixed motor/VFD cooling loads, auxiliary demand, and training, inference, and idle workload profiles. A rule-based supervisory state machine represents grid, battery, and diesel operating states and captures temporary IT-load isolation and workload-dependent restoration following voltage recovery. The model is implemented in MATLAB/Simulink using positive-sequence phasor-domain simulation and evaluated under fault-induced delayed voltage recovery (FIDVR) conditions. For a study system with a 100~MW conventional composite load and a 200~MW data-center load, the long-duration voltage recovery is governed mainly by conventional stalled-motor thermal dynamics, while the first 100~ms after fault clearance is strongly workload-dependent. Training and inference produce approximately 50–52~mpu voltage variation and 751–755~MW active-power variation, compared with approximately 36~mpu and 654~MW for idle operation. The results demonstrate the value of incorporating data-center-specific load dynamics into transmission-level stability studies while highlighting the need for future measurement-based validation and more detailed converter-level modeling.

The precise locating of trapped victims is arguably the most challenging issue in SAR operations, particularly when infrastructure is destroyed and SAR teams only have low-power beacon signals from smartphones to search with. This paper presents a framework for centralized and cooperative UAV-based localization with deep learning-based channel classification and 3D environment-adaptive target estimation. A CNN-LSTM classifier is employed based on a dataset generated at 867.5 MHz with NYUSIM. This classifier labels the individual links of a UAV to a target. Then, these labels activate specific localization solvers: a first-order Taylor-expanded WLS method for LOS settings, an LSRE-SOCP method with iterative refinement for NLOS, and a hybrid projection-based scheme for mixed environments. Through exhaustive simulations, this paper shows that the proposed framework significantly reduces runtime and yields high localization accuracy even when the transmit power and path-loss conditions are unknown. The method can be used in diverse propagation scenarios, making it easy to deploy in reality. The whole NYUSIM-generated dataset is made publicly available to support future research on disaster-aware wireless localization.

Deep learning (DL) classifiers trained on I/Q samples achieve high accuracy for IEEE 802.11 protocol identification of overlapping signals, but their performance degrades sharply when channel conditions at deployment differ from those encountered during training. We present WhiteNet, a framework that addresses the problem of channel variability in I/Q samples. The central idea is spectral whitening, a physics-grounded preprocessing step that suppresses frequency-selective fading while preserving protocol-discriminative features. To reduce dependence on costly multi-transmitter over-the-air captures for training, we complement it with a synthetic overlap mixer featuring a physically accurate per-transmitter channel and shared-receiver signal chain for pre-training without extensive field data collection. On public over-the-air IEEE 802.11 data, WhiteNet closes a substantial portion of the accuracy gap caused by unseen channel conditions while using 7.7 times fewer parameters than the prior state of the art, and optionally distills to compact variants for coarse spectrum awareness on power-constrained edge devices.

This paper assesses the accuracy of the "added length method" (also known as port extension or channel offset) for de-embedding coaxial-to-waveguide transitions in the X-band, comparing it to full waveguide calibration on both Keysight and Rohde & Schwarz VNAs. The study examines the trade-offs between cost and measurement accuracy, highlighting the feasibility of using these mathematical correction techniques in the absence of dedicated waveguide calibration kits or full S-parameter characterizations of the transitions. The analysis concluded that when using coaxial-to-waveguide transitions the added length method produces accurate results for transmission measurements only.

This work addresses joint access point (AP) selection and precoding for sum-rate maximization under statistical channel state information (CSI) in multi-AP multi-user systems. To this end, we propose two approaches. The first method is an iterative alternating optimization algorithm that updates the precoding vectors via the stochastic WMMSE (SWMMSE) algorithm and the assignment variables via a projected gradient descent step. The second method is a graph neural network (GNN)-based framework that solves the same problem in a single forward pass during inference. Building on an attention-based Edge-GNN architecture, we extend it to a multi-AP scenario, enabling the joint learning of assignment variables and precoding vectors from statistical CSI alone. Results show that the GNN outperforms the iterative algorithm across the tested signal-to-noise ratio (SNR) range and generalizes to varying numbers of users with comparable performance. Both approaches are also compared to various baseline techniques.

We propose a neural cyclic redundancy check (CRC) predictor for the 5G New Radio (5G NR) physical uplink shared channel (PUSCH) that enables early link-adaptation decisions for Ultra-Reliable Low-Latency Communications (URLLC). The predictor combines a lightweight convolutional neural network (CNN) with a fixed front-end that extracts multi-scale time-frequency energy features from the received signal and least-squares channel estimates. We investigate two complementary front-end realizations - a wavelet scattering front-end built from fixed Gabor filters, and an FFT-based scattering front-end that applies bandpass masks in the frequency domain with geometric scale spacing. Drawing on neural-receiver design principles, the predictor estimates the post-decoding CRC outcome directly from the received resource grid, bypassing the conventional equalization and decoding chain. We further introduce the modulation and coding scheme (MCS) index as an auxiliary conditioning input that adapts the decision boundary to the operating code rate. Experiments on a multi-MCS 5G NR PUSCH dataset show that the hybrid scattering predictors substantially outperform a pure CNN baseline, with MCS conditioning further improving reliability across varying channel conditions. Both front-ends are compatible with GPU-accelerated inference and are lightweight enough to be deployed within a real-time baseband pipeline. We further demonstrate an evidential deep learning extension that quantifies epistemic uncertainty in a single forward pass using a conservative decision rule.

In this work, we introduce and experimentally validate a numerical model for a Multiple-Input-Multiple-Output Distributed Acoustic Sensing (MIMO-DAS) system that accounts for dynamic perturbations of fiber birefringence and of the common optical phase of the backscattered signal (or polarization-averaged phase, shared by both polarization tributaries). The MIMO-DAS system probes the fiber using polarization-multiplexed constant-power coded sequences that are suited for coexistence of DAS with WDM data transmission over the same fiber. We study the effect of both axisymmetric and anisotropic events on the two quantities. We demonstrate, through numerical simulations and lab experiments, the estimation of effective birefringence magnitude in static conditions, and the joint estimation of common phase and effective birefringence magnitude in the case of dynamic longitudinal strain and anisotropic transverse strain. This allows for event discrimination and increased sensitivity to disturbances that act transversely on the fiber, since polarization will be responsive to perturbations that break cylindrical symmetry, while the phase will strongly respond to longitudinal strain.

Adaptive algorithms are widely used for estimation of linear time-varying systems, such as communication channels. Their tracking performance depends on the level of noise, characteristics of time variations, and the algorithm parameters. Optimizing these parameters and predicting the algorithm performance is an important task. In this paper, we present an approach for analysing the tracking performance of recursive least squares (RLS) adaptive algorithms in channels with time variations described as wide-sense stationary uncorrelated scattering (WSSUS) random processes characterised by a power spectral density (PSD). We focus on exponential RLS and sliding-window RLS (SRLS) algorithms, for which general formulas for the mean square deviation (MSD) as a measure of the tracking performance are obtained in terms of spectral moments of the PSD. As examples, they are specified for random processes with uniform, Jakes' and autoregressive PSDs. These results are further generalized to RLS algorithms with delays, e.g. processing delays or delays introduced in non-causal adaptive RLS algorithms, and to the SRLS-L algorithm with approximation of channel time variations using Legendre polynomials. Numerical examples show good match between the analytical MSD and simulation results.

Task offloading in space–air–ground integrated networks (SAGIN) yields non-convex signomial or polynomial programs with cubic couplings. Sequential geometric programming (SGP) approximates them via exponential cone representations, which exceeds the second-order cone programming (SOCP) ceiling of embedded code generators such as CVXPYgen. We derive a difference-of-convex (DC) representation exactly certified over the reals by quantifier elimination and apply the convex–concave procedure (CCP), whose SOCP subproblems remove this structural obstacle to future embedded code generation. Comparisons with the BARON global solver show that SGP and CCP both attain near-global solutions. CCP further reduces the average solution time from SGP's $0.1012$~s to $0.0113$~s, an $8.9$-fold speedup.

Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexity. Recognizing that radio propagation is intrinsically governed by the physical geometry, we develop a 3D Gaussian splatting for environment-aware beamforming (GSBF) pipeline based on multi-modal data, which characterizes the environment through a persistent 3D Gaussian representation. Specifically, GSBF models the environmental scattering response with reciprocity-preserving bidirectional spherical Gaussian (Bi-SG) kernels and performs two-sided electromagnetic rasterization to render an angular propagator map. The rendered map is then aggregated through an over-complete array-manifold dictionary and projected to the constant-modulus beamformers, thereby synthesizing beams directly from the access point (AP) pose and user position without online instantaneous CSI. Simulations demonstrate that GSBF consistently outperforms baselines such as exhaustive beam alignment (EBA) with lower latency.

Applying foundation models to the radio frequency (RF) domain presents unique challenges due to the intrinsic physical complexity of raw I/Q signals and the extreme heterogeneity of spectral data. In this paper, we present Radio-FM, a scalable family of foundation models designed for universal radio signal representation learning. Unlike standard architectures, Radio-FM employs dual-channel processing specifically optimized for I/Q independence while capturing cross-channel interactions through a lightweight attention mechanism. To scale pretraining across heterogeneous multi-source corpora with highly variable sequence lengths, we propose a token-budgeted dynamic batching strategy coupled with channel-independent masked reconstruction. We pretrain Radio-FM on a diverse collection of 15 datasets spanning modulation, radar, and communication domains, and rigorously evaluate it on 15 downstream benchmarks. Experimental results show Radio-FM achieves state-of-the-art performance on 13 of 15 benchmarks, consistently improving across modulation, radar, emitter identification, wireless technology recognition, and wireless interference identification. Notably, it exhibits superior few-shot transferability, significantly outperforming existing baselines in data-scarce regimes, validating its potential as a general-purpose backbone for radio signal understanding.

Respiration provides a continuously available window into physiological state and behavior. However, monitoring it outside controlled settings remains challenging because a wearable system must capture small body deformations while remaining comfortable, low power, and robust to changes in posture and motion. We present a compact non-invasive respiratory sensing system based on a force-sensitive resistor (FSR) embedded in an abdominal belt and integrated with a custom Bluetooth Low Energy acquisition board. The system combines a simple piezoresistive readout with a mechanical holder designed to transfer abdominal expansion to the sensor without analog amplification. We evaluate the complete sensing pipeline across multiple breathing patterns and body positions. In stationary settings, the recorded signals exhibit consistent amplitude changes and recurring peak-to-peak timing across breathing maneuvers; under light movement, these variations remain visible despite motion-induced baseline shifts. We further design a five-phase stress-induction protocol and collect respiratory recordings from 12 participants. Using interpretable time-domain features and standard classifiers, we examine whether the acquired signals distinguish relaxation from stress-induction phases. In this preliminary experiment, the best-performing model achieves 88.0% test accuracy, indicating that the extracted respiratory features distinguish stress-induced phases from relaxation phases in this dataset. Overall, our results show that the proposed platform enables real-time respiratory monitoring across diverse daily-life scenarios and captures respiratory changes that distinguish stress-induction from relaxation phases, supporting its potential for affective-computing applications.

Real-time extended reality (XR) services impose stringent throughput, latency, and reliability requirements. An XR media unit is commonly segmented into a protocol data unit (PDU) set that is useful only when all required PDUs are delivered within its delay budget. Existing PDU set-aware medium access control (MAC) schedulers generally prioritize transmissions according to urgency or progress, without explicitly accounting for whether a PDU set can still be completed on time. In this paper, we propose a Lyapunov-based completion-aware MAC scheduler that jointly captures the set-level completion dependency, deadline urgency, and completion feasibility of each head-of-line PDU set. Long-term quality-of-service (QoS) requirements are represented through virtual debt queues, and resources are allocated according to their marginal contribution to the timely-completion probability of each head-of-line PDU set. These components are integrated into a drift-plus-penalty scheduling metric that balances long-term reliability requirements against current completion opportunities. System-level simulations demonstrate that the proposed scheduler improves PDU set delivery reliability and lower-tail performance under resource contention, while providing robust performance across heterogeneous traffic demands and network conditions.

Quantum state tomography (QST) has attracted considerable attention due to its fundamental role in quantum information processing. In this paper, we develop a unified theoretical framework for analyzing the sample complexity of structured QST under noisy observations arising from state preparation noise, measurement noise, and finite-shot statistical noise. The proposed framework applies to a broad family of structured quantum-state classes, including general mixed states, sparse states, low-rank states, matrix product states (MPSs), matrix product operators (MPOs), projected entangled-pair states (PEPSs), and projected entangled-pair operators (PEPOs), while further introducing physically consistent structured models—including low-rank and sparse states, low-rank MPOs (LR-MPOs), and low-rank PEPOs (LR-PEPOs)—that simultaneously exploit low-dimensional structures and preserve the physical constraint. Within this framework, we derive unified non-asymptotic sample complexity guarantees for two constrained least-squares estimators under noisy observations: a noise-aware estimator that incorporates the calibrated noise model and a noise-unaware estimator based on the ideal Born measurement model. For the noise-aware estimator, we derive unified trace-norm recovery guarantees that explicitly characterize the dependence of the sample complexity on three fundamental quantities: the complexity of the underlying structured state class, the complexity of the measurement ensemble, and the state preparation and measurement noise levels. For the noise-unaware estimator, we establish a unified non-asymptotic recovery guarantee consisting of a statistical error term and an additional deterministic bias term arising from the mismatch between the assumed reconstruction model and the noisy observation process.

Accurate channel estimation is essential for coherent transmission in ultra-massive multiple-input multiple-output (UM-MIMO) systems, where near-field propagation and high-dimensional spatial channels impose substantial signal processing challenges. Movable antenna architectures increase the estimation complexity further due to geometry-dependent channel variations. Existing approaches struggle to balance accuracy and complexity, motivating the use of environment-dependent propagation structures for efficient estimation. To this end, this paper proposes a unified channel map-based channel estimation framework for UM-MIMO systems, which integrates movable planar array reconfiguration and near-field spherical-wave modeling to support geometry-aware line-of-sight (LoS) estimation and efficient non-LoS (NLoS) recovery. A channel map-based LoS estimator is proposed combining coarse user position information with a Fisher information-guided antenna placement strategy. Two efficient NLoS estimation methods are also presented, including a sketch-based reduced-subspace estimator for low-complexity processing and a channel map-based estimator that leverages scatterer location information for near-optimal performance. The framework further incorporates visibility-region modeling and a structural similarity-based pilot assignment strategy for multi-user scenarios. Simulation results show that the proposed channel map-based framework improves estimation accuracy, reduces computational overhead, and enhances scalability compared with state-of-the-art benchmarks without channel maps.

Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to compare because preprocessing, EEG segmentation, feature design, classifier choice, and validation protocols vary across studies and are often evaluated in isolation. This variability limits the derivation of robust pipeline recommendations. This paper presents a leakage-safe empirical benchmark for resting-state EEG-based dementia classification and uses it to identify a practical best-practice pipeline. Using the public OpenNeuro ds004504 dataset, the benchmark evaluates artifact correction, fixed-length EEG segmentation, training-only augmentation, multi-domain feature extraction, fold-internal feature selection, classical machine-learning (ML) classifiers, subject-level aggregation, and interpretation under leave-one-subject-out (LOSO) validation. The best-performing pipeline in this benchmark combines Artifact Subspace Reconstruction (ASR) followed by Independent Component Analysis (ICA), 10 s EEG epochs with training-only amplitude scaling and Gaussian-noise augmentation, spectral, complexity, and pairwise connectivity features, mutual-information top-100 selection, linear support vector machine classification, and mean-probability aggregation. It achieves 87.69 percent accuracy, 88.89 percent F1-score, and 91.20 percent AUC on Alzheimer disease (AD) versus cognitively normal controls (CN) classification while retaining 100 features from 1596 raw descriptors. The results support compact multi-domain EEG features as an interpretable and leakage-safe baseline for subject-level dementia classification.

Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the Channel State Information variations of dedicated WiFi, affected by human bodies obstructing the WiFi signal propagation path. However, the adoption of WiFi for PHS has some drawbacks, related to power consumption, large-scale deployment costs and interference with other networks in nearby areas. Bluetooth technology and, in particular, its low-energy version Bluetooth Low Energy (BLE), represents a valid candidate solution to the drawbacks of WiFi, thanks to its Adaptive Frequency Hopping (AFH) mechanism. This work proposes the application of a Deep Convolutional Neural Network (DNN) to improve the analysis and classification of the BLE signal deformations for PHS using commercial standard BLE devices. The proposed approach was applied to reliably detect the presence of human occupants in a large and articulated room with only a few transmitters and receivers and in conditions where the occupants do not directly occlude the Line of Sight between transmitters and receivers. This paper shows that the proposed approach significantly outperforms the most accurate technique found in the literature when applied to the same experimental data.

This work presents a compressive sensing-based approach for estimating the micro-motion parameters of targets with rotating components, such as small unmanned aerial vehicles (UAVs) with propellers, using fewer measurements than conventional methods. A multiple-input-multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar employing a randomly spaced sparse linear antenna array is utilized. Random sequences of linear frequency-modulated (LFM) chirps are transmitted to enable random sampling in the slow-time domain. At first, the range, velocity, and angle of arrival (AoA) of the targets are estimated to identify the bulk motion. A three-dimensional point target response (3D-PTR) is then constructed using the estimated parameters and subtracted from the total radar return to extract the micro-Doppler signatures associated with target rotation. These residual signals are processed within a compressive sensing (CS) framework using the one-dimensional orthogonal matching pursuit (1D-OMP) algorithm to jointly estimate the propellers' rotation frequencies and blade lengths with the help of a parametric dictionary. The proposed approach is also extended to a multi-target scenario. Simulation results demonstrate that the proposed approach accurately estimates micro-motion parameters with limited measurements in the slow-time and the spatial dimensions, validating its potential for UAV detection applications. These estimated parameters are then used to distinguish among various UAV motion types using a classification framework based on a set of decision rules.

In Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) near-field (NF) sensing, target motion induces an antenna-dependent bistatic Doppler variation across the array aperture. Ignoring this spatial Doppler variation leads to a model mismatch that degrades NF localization. In this paper, we propose a low-complexity recursive framework for joint radial/transverse velocity estimation and Doppler-aware localization. Initialized by a constant-Doppler coarse localization, the method alternates between closed-form Least Squares Estimator (LSE)-based velocity estimation and antenna-dependent Doppler-aware localization refinement. Simulation and measurement results demonstrate the effectiveness of the proposed framework against two benchmark methods. Compared with a low-complexity constant-Doppler baseline method, the proposed algorithm improves range, angle, and radial velocity estimation results, while also enabling transverse velocity estimation. In the measurement results, the overall localization error decreases from 0.268 m to 0.064 m. The radial and transverse velocity estimation errors are 0.032 m/s and 0.069 m/s, respectively. Compared with a high-complexity exhaustive four-dimensional (4D) Maximum Likelihood Estimator (MLE), the proposed method achieves comparable velocity estimation results while yielding a more accurate localization result when the 4D MLE has a practical finite search grid.

Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously explore the context, store intermediate state, verify evidence, and produce the final answer. This becomes particularly difficult in tasks that require extraction, counting, ordering, or multi-hop reasoning, where an early mistake can propagate until the final response. In this work, we propose Chained Recursive Language Models (Chained RLM), an inference-time architecture, in which the same underlying model is called repeatedly as a sequence of fresh reasoning roots. Each root receives the original problem and context, but does not inherit the full conversational history. Instead, it receives a compact plain-text summary, a plain-text blackboard, and some durable task-specific artifacts written by predecessor roots. The motivation is to manage the context by chopping into partial tasks rather than one large inference response; in each staged computation, intermediate artifacts can be inspected, corrected, and extended by a later fresh inference by the same model. We describe the system model, handoff mechanism, artifact workspace, and evaluation protocol for this system. We study when fresh-context artifact continuation gives a measurable gain in accuracy over direct LLM answering even with recursive tool-calling.

Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals. Existing wireless foundation models typically pretrain on channel tensors using masked reconstruction over subcarriers, antennas, or time but ignore the physical characteristics of wireless propagation. In this work, we propose to instead use multipath propagation as the fundamental pretraining object. We present MultiPathFormer, an autoregressive foundation model that represents each transmitter-receiver link as an ordered sequence of continuous-valued path tokens and pretrains with next-path prediction. We introduce an Environmental RAG (retrieval-augmented generation) mechanism and a first-path codebook on top of the transformer backbone, leveraging environment knowledge to improve path statistics estimation like delay and power by up to 59%. MultiPathFormer pretrained on 27 environments transfers to unseen users and, after scenario-specific fine-tuning, outperforms training the corresponding models from scratch in new environments. Across downstream tasks, it outperforms SOTA channel-based foundation models, achieving 5.57 m mean localization error, 0.914 top-3 beam accuracy, 0.994 line-of-sight classification accuracy, and 0.561 channel estimation NMSE. These results show that path-level pretraining can learn reusable representations of wireless propagation.

Many existing time-series Interferometric Synthetic Aperture Radar (TS-InSAR) software tools have limitations, including restricted geographic applicability, commercial licensing, and incomplete end-to-end processing support. Although GMTSAR avoids some of these constraints, it still requires substantial manual intervention and C-shell commands, lacks a user-friendly graphical interface, and omits important steps such as interferogram network pruning and anchoring of unwrapped interferograms. This paper introduces DefoEye (v1), an open-source Python-based software package that wraps GMTSAR and provides a unified, user-friendly TS-InSAR workflow for Sentinel-1 data. DefoEye supports parallel job execution, interferogram network pruning, and multiple anchoring options. Its performance was evaluated from 2020 to 2024 in four regions with different geological settings, deformation mechanisms, and atmospheric and climatic conditions. In Bologna, Italy; Gotland, Sweden; and Houston, USA, DefoEye results were compared with observations from 10 GNSS stations and showed strong agreement, with RMSE values of 4.3-11.9 mm and Pearson correlation coefficients of 0.63-0.95. In Karaj, Iran, where GNSS observations were unavailable, DefoEye was compared with other widely used processing tools and achieved similarly close agreement, with an RMSE of 4.8 mm/yr and a Pearson correlation coefficient of 0.98. These results demonstrate that DefoEye provides reliable TS-InSAR products for geological, hydrological, and environmental applications.

Recent generative video codecs (GVCs) have achieved impressive reconstruction fidelity at ultra-low bitrates (< 0.02 bits per pixel) by compressing the tokens from generative tokenizers. However, existing GVCs generally require considerable computation time and model complexity, which hinder their deployment on compute-limited devices and in real-time applications. To bridge this gap, we systematically identify the computational bottlenecks and propose GVC-RT, which redesigns the generative latent coding framework to realize real-time video coding without sacrificing compression performance. Specifically, built on a pretrained lookup-free quantization (LFQ) tokenizer, GVC-RT adopts an asymmetric architecture that directly learns to match the LFQ latent distribution, while generative-space alignment is enforced via a regularization loss term only during training. In this manner, we bypass heavy tokenization and entirely remove the complex feature-alignment process at inference time. Moreover, we further introduce a lightweight de-tokenizer architecture to resolve the final latency bottleneck during decoding. Experimental results demonstrate that GVC-RT outperforms the previous SOTA model, GLC-Video, with average BD-rate savings of 12.4% and 48.8% in terms of DISTS and LPIPS, while achieving encoding/decoding speeds of 123.1/55.1 fps for 1080p video. The code is at https://github.com/semcomm/GVC-RT.