Papers

Each repeated pilot block is typically treated as a separate channel-estimation problem, although much of the angle–delay state persists across updates. This paper defines an electromagnetic twin (ET) as a recursively synchronized signal-processing state containing channel state information (CSI), coefficientwise uncertainty, support confidence, and age. A channel knowledge map may initialize this state; subsequent pilots predict, correct, validate, and store the state. We formulate an anchored-window inverse problem that yields online uncertainty-weighted correction and fixed-lag smoothing as two convex operating modes of a persistent-state estimator. Uncertainty-aware ET FISTA (UET-FISTA) gives a zero-delay update, while an alternating direction method of multipliers (ET-ADMM) jointly refines a short window when delayed CSI is acceptable. Held-out pilots choose update, complete refresh, or hold under a prescribed family-wise false-acceptance probability, preventing an inaccurate stored state from being reused blindly. Conditional Bayesian Cramér–Rao bounds quantify the information supplied by the stored state and by future pilot blocks. With 20% pilots, UET-FISTA reaches $-11.55$ dB normalized mean-square error, compared with $-3.42$ dB for static FISTA. Over 200 independent 50-epoch trajectories, three-epoch smoothing improves matched-state NMSE from $-10.28$ to $-13.41$ dB. On 40 off-grid QuaDRiGa trajectories, online UET-FISTA improves static recovery from $-1.37$ to $-1.88$ dB and effective rate from 1.688 to 1.919 bit/s/Hz. These results identify when persistent CSI reduces pilot demand and when the receiver should instead refresh or preserve its state.

The generalized finite rate of innovation~(GenFRI) framework aims at reconstructing finite-rate-of-innovation~(FRI) signals measured through a noisy linear measurement model. GenFRI has been recently recast as a structured low-rank optimization problem and the Cadzow projected gradient descent~(CPGD) algorithm has been suggested to solve it. While CPGD works well in practice, only qualitative local convergence guarantees have been established. We revisit GenFRI in the light of the regularization by denoising framework, recasting it as an optimization problem whose solutions lie in the fixed-point set of the Cadzow denoiser. We show that no algorithm in this family can enjoy global guarantees, and establish instead that the Cadzow denoiser is quasi-nonexpansive on an explicit neighborhood of the FRI model set, whose radius is governed by the conditioning of the underlying Dirac stream. Building on these results, we propose the generalized CPGD~(GCPGD) algorithm and prove its convergence from any initialization within an explicit basin of attraction, together with a reconstruction error bound proportional to the noise level. Numerical experiments validate the predicted contraction rates, recovery thresholds, and run-time certificates, and show that a single run of GCPGD outperforms state-of-the-art GenFRI algorithms.

Reconfigurable intelligent surfaces (RISs) enable programmable wireless propagation environments, offering new opportunities for cognitive radio networks (CRNs) to improve spectrum utilization, enhance spectral and energy efficiency, and operate reliably under low signal-to-noise ratio conditions. By combining the complementary strengths of RISs and CRNs, RIS-assisted CRNs (RCNs) have emerged as a promising architecture for future 6G wireless systems. Despite their growing importance, a comprehensive survey of this rapidly evolving field has been lacking. This paper fills this gap by providing a systematic and comprehensive review of RCNs. The paper first introduces the fundamentals of CRNs and RISs, including dynamic spectrum access models, spectrum sensing techniques, RIS operating principles, and RIS architectures. It then examines the design of RCNs, covering their system architectures, deployment strategies, channel estimation, spectrum access mechanisms, communication protocols, and the joint optimization of RIS and CRN parameters. Next, the existing literature is organized into six major research directions: performance analysis, resource allocation and optimization, secure RCNs, active RISs, simultaneously transmitting and reflecting RISs, and machine learning-enabled RCNs. Finally, the paper discusses key research challenges and future directions, including scalability, practical deployment, integration with emerging 6G technologies, coexistence with evolving network architectures, standardization, and security and privacy. This survey provides a unified reference for researchers and practitioners and establishes a roadmap for the future development of RCNs.

Voice has been proposed as a low-cost screening signal for chronic obstructive pulmonary disease (COPD). COPD is strongly age-associated and voice changes with age, thus such results admit a trivial alternative explanation. We re-evaluate a public sustained-phonation corpus ($1246$ recordings, $68$ participants) under a strictly participant-level protocol. We therefore evaluate on repeatedly drawn age-matched cohorts and report the discrimination achieved by the confounders themselves on those same cohorts. Where raw (unmodelled) age ($0.510$ $[0.469, 0.551]$) and raw gender ($0.479$) are both measured at chance, acoustic models excluding age retain ROC-AUC $0.717$ $[0.552, 0.859]$ and average precision $0.747$ $[0.581, 0.892]$ against a one-to-one baseline of $0.5$, whereas models containing age fall to $0.531$–$0.679$. The separation is reproduced by two further learners with fixed hyperparameters. Two findings have broader methodological implications: models trained with age transfer less effectively to an age-balanced target cohort than otherwise identical models trained without age, and fourteen classical voice-quality and perturbation measures achieve comparable discrimination to a $55$-dimensional combined representation. We conclude that a non-age acoustic signal is present, that confounding by recording conditions cannot be excluded from the released features, and that the evaluation protocol in standard use cannot distinguish these possibilities.

Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes unavoidable, which may cause collisions, confusions, and multiple forms of modular interference. Jointly mitigating these effects gives rise to a large-scale, multi-objective combinatorial optimization problem that is difficult to solve efficiently at practical network scales. In this work, we propose a congruence decomposition framework with neural block solvers for large-scale PCI assignment. The proposed decomposition exploits the arithmetic structure of PCI values to decouple multiple modular interference objectives into a collection of blockwise Min-$k$-Partition subproblems, followed by a graph coloring procedure to resolve PCI conflicts. For the resulting NP-hard Min-$k$-Partition subproblems, we develop neural block solvers by parameterizing their relaxed quadratic formulations with graph neural networks, enabling efficient optimization at large scales. Discrete assignments are recovered through conditional expectation rounding with theoretical guarantees. Experiments on synthetic cellular graphs and real-world 5G networks show that the proposed method consistently outperforms existing modular-interference-aware baselines in modular interference reduction, conflict elimination, and computational efficiency.

The standard shortcut in slotted-waveguide array design – solve one radiating stick, multiply its embedded pattern by the array factor of the intended aperture distribution, and accept the product as the array pattern – is tested against a full-wave solution of the same metal. For a 16x16-slot planar array in WR-90 synthesised to a -30 dB Taylor illumination, the shortcut underestimates the transverse-plane sidelobe level by 1.06 dB at the design frequency and by 2.22 and 2.84 dB at the edges of a 2% band: a spread of 1.78 dB, larger than the mid-band error itself. Every sidelobe figure carries +/-0.11 dB of mesh spread from a convergence study on the production geometry. The error is signed, is worst at the band edges where the design has least margin, and falls only from 1.58 dB to 1.06 dB when the aperture is doubled from eight sticks to sixteen, which identifies the edge elements as its source. The array was made analysable by a thirteen-point single-slot full-wave calibration: over the design's offset range the handbook closed form for resonant length moves 3.9 micrometres where the measurement moves 143.9 micrometres, and Stevenson's conductance is shown to overestimate by a nearly constant 5.16%.

This paper presents a phased-array transmit beamformer design for beampattern matching and interference suppression. We propose an optimization framework jointly addressing these two objectives. The resulting non-convex problem is solved via alternating minimization, where each subproblem admits a closed-form solution, one of which recovers the classical maximum-gain and null-steering phased-array beamformers. To design an algorithm ensuring theoretical convergence guarantees, the beamformer design is then reformulated as a non-convex set feasibility problem, closely related to the original formulation. This problem is addressed using a provably convergent projected gradient descent method and further enhanced with acceleration techniques that substantially improve empirical convergence. Extensive simulations validate the excellent performance and computational efficiency of the proposed method.

Integrated sensing and communication (ISAC) has been identified as one primary usage scenario in the sixth-generation (6G) network. While techniques to preserve information privacy, such as cryptography, have been widely investigated, how to preserve sensing privacy is still an open problem in the literature. This paper makes an early attempt to tackle the above issue. Specifically, we consider a localization system consisting of a multi-antenna transmitter, termed Alice, a single-antenna legitimate receiver, termed Bob, and a single-antenna illegitimate receiver, termed Eve. To allow Bob to estimate Alice's angle-of-departure (AOD) but prevent Eve from performing this task based on Alice's signals, this paper proposes a novel antenna selection and permutation based transmission strategy for Alice. Under this scheme, Alice carefully selects a subset of antennas and permutes their indices to establish a specific pilot-antenna mapping for transmission. Similar to cryptography for information privacy, such a mapping will serve as the secret key to preserve localization privacy. In the special case without noise at Bob and Eve, we manage to find out all the antenna selection and permutation solutions such that with this key (knowledge about the exact pilot-antenna mapping), Bob can uniquely estimate Alice's AOD, while without this key, Eve can estimate multiple AODs of Alice that can lead to its received signals. In the noisy case, numerical results are provided to show that our scheme can confuse Eve to make inaccurate AOD estimation as well.

Integrated sensing and communications (ISAC) is a key enabler for uncrewed aerial vehicles (UAVs) in the low-altitude economy. This paper proposes an ISAC waveform that embeds a unique word (UW) into orthogonal chirp division multiplexing (OCDM), termed UW-OCDM, together with corresponding communication reception and cooperative sensing schemes for high-mobility UAV scenarios. For communication, the embedded UW enables timing synchronization and Doppler estimation and compensation without requiring a separate synchronization sequence. A sparse spatio-temporal channel estimation method exploits the common channel support across multiple receive antennas and consecutive UW observations to support reliable data demodulation. For sensing, the deterministic UW serves as a shared prior that allows distributed base stations to construct sensing dictionaries locally without exchanging random payload symbols in real time. A hierarchical multi-target detection and tracking algorithm integrates direct-path interference suppression, kinematic prediction, multi-candidate screening, off-grid refinement, residual verification, and successive interference cancellation for robust localization with reduced search complexity. Simulation results demonstrate reliable communication and localization in highly dynamic UAV scenarios, while the proposed framework retains low-complexity frequency-domain equalization and reduces transmit-reference sharing overhead and multi-static localization complexity.

High-altitude platforms (HAPs) are key enablers of next-generation non-terrestrial networks (NTNs), offering wide coverage, long endurance, and rapid deployment. Despite these advantages, current NTN designs remain satellite-centric and rely on terrestrial cellular assumptions, limiting flexibility and scalability. To overcome these limitations, this article proposes a multi-layer HAP-centric flying ad-hoc network (FANET). In this framework, HAPs are integrated with distributed uncrewed aerial vehicles (UAVs) to form a standalone, cell-free (CF) non-terrestrial system capable of autonomous operation. The layered architecture consists of an inter-HAP ad-hoc layer, a HAP-to-UAV cooperative layer, and a UAV-to-ground access layer, collectively enabling aerial connectivity, adaptive coverage, and interference-aware user access. Unique challenges for each layer are analyzed, including inter-HAP connectivity, FANET co-existence with terrestrial networks (TNs), and user access under heterogeneous conditions. Moreover, the article introduces enabling strategies such as fast beam alignment for high data rate connectivity, uncoordinated FANET/TN co-existence, and user localization and environment classification. Validated by three case studies, the discussion also outlines standardization pathways. The results highlight HAP-centric FANETs as a foundation for resilient, scalable, and application-oriented 6G NTN deployments.

An uplink pinching-antenna system (PASS) is considered, where a segmented dielectric waveguide is employed to serve multiple users and one pinching antenna (PA) is activated on each segment. Extensive waveguide deployment reduces user-to-PA path loss but also creates differential propagation delays among the PA-assisted paths spanning the large waveguide aperture. These delays are retained, and a Toeplitz block model is derived to characterize the resulting frequency-selective (FS) channel. For infinite-blocklength transmission, the achievable single-user rate and the multiuser sum-rates under joint and parallel decoding are characterized. For carrier-phase-aligned single-user PA placement, the frequency-flat (FF) model is shown to overestimate the exact FS rate. The difference between the rates computed under the FF channel model and the exact FS channel model is referred to as the FF rate overestimation, and its dependence on the waveguide-aperture-induced delay spread is characterized. For small delay spreads, the FF rate overestimation is shown to increase approximately quadratically with the delay spread, whereas a derived lower bound reveals that the overestimation grows at least double logarithmically with the waveguide aperture, or equivalently with the delay spread, in the large-aperture regime. For finite-blocklength transmission, achievable rates are derived under a practical cyclic-prefix single-carrier frequency-domain equalization framework. An element-wise algorithm is developed to optimize FS-aware PA placement in both transmission regimes. The analytical findings demonstrate that differential delays across the extensively deployed waveguide can cause significant frequency selectivity and that the proposed FS-aware PA placement method substantially improves the achievable rate over placement based on the FF approximation, especially for large bandwidths and apertures.

Multistatic ranging with mobile receivers enables good tracking performance due to the combined adaptive sensor geometry. However, environments that contain signal obstructing obstacles require receiver trajectory planning to maintain line-of-sight (LOS) conditions with transmitters and the target of interest. In this letter, we develop a non-myopic receding-horizon framework for multistatic tracking. It uses convex collision-avoidance constraints and a control objective that focuses on maintaining good LOS signal conditions, taking into account future obstructions. We demonstrate the efficiency and tracking accuracy of the method via a numerical experiment.

This paper proposes a Square-Root Transformed $L_1$-norm ($SR\text{-}TL_{1}$) sparse recovery framework for single-measurement-vector (SMV) direction of arrival (DoA) estimation under highly-coherent overcomplete dictionaries with angular-dependent array imperfections. The proposed framework combines the square-root Least Absolute Shrinkage and Selection Operator (square-root LASSO) framework which is known to be robust against noise variance with the Transformed $L_1$-norm ($TL_1$-norm), a non-convex penalty that shows a stronger recovery performance than the classical convex $L_1$-norm under highly-coherent dictionaries. We use the Difference of Convex Algorithm (DCA) along with the Alternating Direction Method of Multipliers (ADMM) algorithm to obtain simple, closed-form update rules and provide an efficient implementation that leverages the low-rank nature of the Gram matrix of the dictionary so as to obtain a computational complexity of at most $\mathcal{O}(MN)$ per iteration where $M$ is the array size and $N$ is the length of the dictionary. We additionally provide a convergence guarantee for the DCA iterates of $SR\text{-}TL_{1}$. Experimental verification of the proposed framework shows a lower sensitivity of the regularization hyperparameter to the noise variance level and a competitive recovery performance compared to state-of-the-art baselines.

Probing every candidate location at each wireless-map update is costly. This letter develops electromagnetic-twin (ET) beamforming that converts sparse spatial probes into a sum-rate decision. The ET stores a location-dependent angular power spectrum, updates its innovation through graph-regularized estimation, and queries user covariances for projected beam optimization. A rate-sensitivity bound selects subsequent probes by query-weighted posterior-variance reduction. With 1% random probes, ET beamforming achieves $2.69$ bit/s/Hz versus $1.12$ for a static channel knowledge map; with 7% query-aware probes, it reaches $3.03$ bit/s/Hz, within 3.4% of perfect-covariance beamforming.

Acquiring dense channel information over many locations and beams incurs considerable pilot and processing overhead. Radio maps and channel knowledge maps (CKMs) reduce this overhead by reusing site-specific channel information, but their contents must be refreshed when new measurements or environmental observations become available. This paper introduces an electromagnetic twin as an updatable digital representation that uses sparse channel evidence to reconstruct the wireless state requested by communication queries. Rather than replacing radio maps or CKMs, the twin uses a CKM as channel memory, combines it with registered scene information, and regenerates its outputs after each evidence update. We instantiate this idea by completing a two-dimensional channel-gain field from sparse samples and an incomplete floor plan. A learned RF completion backbone recovers the main propagation structure, and a lightweight residual adapter tests whether frozen CLIP features provide useful side information. With $4\%$ measured locations and $55\%$ missing semantic objects, the RF backbone attains $4.44$ dB RMSE, compared with $8.29$ dB for CKM interpolation and $8.38$ dB for an incomplete physics prior. Residual adaptation reduces RMSE by a paired mean of $0.135$ dB (95% confidence interval: $0.100$–$0.169$ dB), but a same-capacity random-feature control is statistically indistinguishable from the CLIP-conditioned adapter. The results therefore support the measurement–update–query loop and lightweight residual correction, while avoiding an unsupported attribution of the correction to visual semantics.

Pinching-antenna systems (PASS) provide a large effective aperture and substantial path-loss reduction at low hardware cost, making them attractive for integrated sensing and communication (ISAC). Under wideband OFDM operation, however, the antennas on each waveguide impose nonlinear, position-dependent group delays on the same baseband signal, giving rise to waveguide dispersion that severely degrades range estimation. To this end, this paper proposes a two-stage ISAC framework comprising communication-aware beamforming and antenna placement followed by a dispersion-aware target localization stage. A fractional-programming beamformer and an element-wise coordinate-descent placement jointly maximize the downlink sum rate under a sensing beampattern-gain constraint, and the resulting optimized beamformer and placement determine the effective sensing channel used by the localization stage. The proposed DisPersion-aware Localization Network (DiPL-Net) detects targets from the received signal via a dispersion-aware score map built on a physics-derived range dictionary. Its convolutional backbone employs dual-kernel residual blocks, each pairing a short kernel matched to the OFDM main lobe with a long kernel matched to the dispersion tail, so as to deconvolve the dispersion and restore a sharp target peak at each true target. Simulation results show substantial localization gains over various sensing baselines while preserving the achievable communication rate.

Upgrading Single-Wire Earth Return (SWER) networks for smart grid capabilities requires a reliable communications technology. Narrowband Power Line Communication (NB-PLC) is a potential low cost solution. Real-world deployment is challenging due to the severe, frequency-dependent attenuation caused by complex earth-return paths and heterogeneous network infrastructure. To accurately characterize the communication channel, this paper develops a high-frequency (up to 300 kHz) digital twin of an operational SWER network. The digital twin integrates a segment-by-segment transmission line model with Vector Network Analyzer (VNA) measurements of physical grid hardware, replacing standard uniform assumptions with empirical component responses. Parametric sensitivity analysis demonstrates that distributed environmental factors, such as soil moisture and line sag, act as uniform magnitude offsets. Conversely, the conductor's magnetic permeability and local injection-transformer impedances dictate the channel's resonant spectral shape. Furthermore, cross-brand analysis proves that utilizing generic transformer models introduces significant prediction errors, confirming that accurate simulation requires manufacturer- and tap-specific data. Validated against in-situ field measurements from three transmitters, this digital twin replicates the path loss and dominant frequency-selective fading of the physical grid. Yielding a Root Mean Square Error (RMSE) between 4.65 dB and 9.73 dB across the three transmit paths, the model provides a practically reliable framework for deploying NB-PLC across rural SWER infrastructure.

Communication intelligence is undergoing two linked transitions: algorithm development is moving from manual model-based design toward foundation-model-assisted generation and evaluation, while deployed systems are moving from offline optimization toward agent-driven online decision and guarded deployment. Existing data-driven and LLM-assisted methods remain primarily design-time tools and cannot cover every future combination of service intent, channel, spectrum, and hardware state. We propose the Agent-Native Metamorphic Communication Fabric, a closed-loop architecture in which an agent observes operating state, selects or generates an explicit communication candidate, evaluates it in a digital twin, applies hard feasibility gates, and deploys it with monitoring and fallback. Three levels bound the scale of change: Level 1 adjusts parameters while preserving algorithm topology; Level 2 switches and configures receiver algorithms while preserving protocol and waveform; and Level 3 reconfigures the waveform or waveform-multiple-access chain while preserving the service contract and safety interface. Simulations validate all three levels. Level 1 improves rate, channel tracking, or quantization energy under fixed topologies. Level 2 selects three-iteration weighted Jacobi, five-iteration diagonally preconditioned conjugate gradient, and direct MMSE in favorable, intermediate, and harsh MIMO regimes; its hardware proxy predicts up to 67.1% energy and 73.3% latency reduction relative to direct MMSE. Level 3 selects CP-OFDM, SC-FDMA, OTFS, filtered OFDM, and SCMA-over-OFDM across five operating regimes and forms continuous switching boundaries under Doppler, spectrum contiguity, load, and RF-power sweeps. These results establish a minimum viable mechanism for verifiable runtime communication adaptation without unconstrained end-to-end learning or arbitrary online code mutation.

Foundation models, i.e., large neural networks pretrained on broad unlabeled data and adapted to many downstream tasks, have reshaped natural language processing and computer vision and are now being explored for the wireless physical layer. Wireless Physical-Layer Foundation Models (WPFMs) aim to learn transferable representations of signals such as channel state information (CSI), in-phase and quadrature (IQ) samples, and spectrograms so that a single pretrained backbone can support tasks ranging from channel estimation and prediction to localization and sensing while using limited task-specific data. This paper provides a dedicated review of WPFMs from learning design to practical deployment. We first establish the theoretical background, covering the neural architectures used for wireless signals, the self-supervised pretraining paradigms of masked modeling, contrastive learning, and generative pretraining, and the fine-tuning strategies that adapt pretrained models to downstream tasks. We then introduce a taxonomy that organizes existing models along five dimensions: architecture family, input modality and tokenization, pretraining objective, model scale and deployment target, and generalization capability. Building on this basis, we review applications across telecommunications, localization, and sensing, and, for each domain, analyze deployment feasibility by mapping model size to the memory, compute, and latency budgets of representative wireless hardware. Finally, we discuss model compression and efficient deployment, summarize the cross-cutting challenges, and outline open research directions. Our goal is to provide a reference that connects pretraining, architecture, and fine-tuning with the practical constraints of wireless systems.

Sparse pursuit after dictionary learning can yield a precise atom support even when its physical interpretation is not justified by the calibration data, especially for highly coherent dictionaries where alternative calibration-compatible dictionaries may assign different physical meanings to the same selected support. We develop resolution-aware physical-support inference that jointly accounts for uncertainty in the learned dictionary and in the representation of a deployment signal. Our cross-dictionary confidence correspondence retains calibration-compatible dictionaries and deployment-compatible sparse representations, then projects the surviving explanations onto physical-support space. For local coherent-atom classes with separation scale s, once the deployment data resolve the coherent-block explanation and its atom support, the minimax physical resolution from N calibration signals satisfies $\delta_{\mathrm{opt}}(N,s)\asymp\min\{s,\frac{1}{\sqrt{N}s^2}\}$, with relative resolution governed by the orientation-information scale $Ns^6$. Deployment replication improves physical localization only when orientation changes cannot be absorbed by adjusting the active coefficients. For computation, we introduce active endpoint bracketing (AEB), an adaptive finite-bank procedure that evaluates only candidates that can still affect the physical report and otherwise safely coarsens or abstains. Finite-bank experiments, including a four-region synthetic application, show that a point-valued plug-in selector can be physically overprecise, whereas AEB avoids unsupported refinement with fewer candidate evaluations.

Integrated sensing and communication (ISAC) is a key enabling technology for 6G wireless networks, but its broadcast nature raises a physical-layer security concern when the sensing target can act as a potential eavesdropper. Although intelligent reflecting surfaces (IRSs) can enhance wireless propagation and improve secrecy, existing secure IRS-assisted ISAC designs are mostly limited to fixed deployments and passive phase control, which offer limited spatial adaptability in line-of-sight-dominated low-altitude scenarios. To address this limitation, we investigate an unmanned aerial vehicle (UAV)-mounted six-dimensional movable IRS-assisted secure ISAC system, where the IRS location, orientation, and reflection coefficients are jointly optimized with the BS beamformer to maximize the secrecy rate under communication quality-of-service (QoS), power, unit-modulus, and visibility constraints. The resulting problem is highly non-convex due to the coupled active/passive beamforming variables and the location-and-orientation-dependent (pose-dependent) channel responses. To solve it efficiently, we develop a three-block alternating optimization (AO) framework, in which the active beamformer, IRS pose, and passive reflection vector are updated via linearized ADMM, warm-started particle swarm optimization, and Riemannian gradient descent, respectively. Simulation results show that the proposed design significantly outperforms fixed-location and orientation-only baselines, highlighting the importance of joint translation, rotation, and phase control for secure ISAC.

Orthogonal Time Frequency Space (OTFS) modulation is a promising waveform for future wireless networks. However, its resilience to RF impairments remains relatively understudied. Low-cost RF front ends are crucial for next-generation wireless systems, yet their performance is often degraded by RF impairments such as transmit IQ imbalance (IQI), phase noise (PN), timing offset (TO), and carrier frequency offset (CFO). Hence, this paper addresses the estimation and compensation of these RF impairments in OTFS systems under high mobility. A unified system model is developed that incorporates TO, CFO, PN, IQI and other channel effects into an effective channel representation. Using the pilot with cyclic prefix (PCP), a low-peak to average power ratio (PAPR) pilot suitable for OTFS, we propose a pilot-aided synchronization and estimation framework. The dual periodicity of PCP is exploited in our proposed TO estimation technique. A maximum-likelihood-based technique is also proposed to jointly estimate the CFO and effective channel using a complex exponential basis expansion model (CE-BEM). Finally, a linear detection model is formulated in the delay-Doppler domain to mitigate residual interference caused by RF impairments. Our simulation results corroborate the efficacy of our proposed synchronization and channel estimation techniques.

Multiple-Input Multiple-Output (MIMO) arrays transmit unique waveforms on each of their elements increasing the degrees of freedom available to synthesize novel transmit beampatterns. The wideband MIMO beampattern shape is determined by the structure of the MIMO Cross Spectral Density Matrix (CSDM) whose elements are the cross-spectra between each waveform in the set as a function of frequency. This paper demonstrates a model for synthesizing wideband MIMO beampatterns using Multi-Tone Sinusoidal Frequency Modulated (MTSFM) waveform sets. The MTSFM waveform's instantaneous phase is a finite Fourier series. The Fourier coefficients are modified to synthesize constant modulus and spectrally compact waveforms whose CSDM closely approximates a desired wideband MIMO transmit beampattern. An optimization routine is formulated that synthesizes MTSFM waveform sets that possess the necessary CSDM structure and is demonstrated using an illustrative design example.

Active sonar target tracking in shallow-water environments is challenging when weak target echoes are embedded in a time-varying background containing structured multipath components. Conventional detect-before-track methods rely on thresholded detections, which may discard weak target evidence or generate false tracks from multipath-induced detections. At low signal-to-noise ratios, track-before-detect filtering can improve tracking performance by exploiting weak target information directly from raw sensor measurements, but directly accounting for the time-varying background leads to a joint target–background inference problem, which is computationally demanding. To address this, this paper develops a physics-motivated and computationally efficient approximation of the raw sensor measurement likelihood for Bernoulli track-before-detect filtering. The multipath components in the background are modeled in the raw sensor measurement domain and recursively tracked using an extended Kalman filter. By neglecting the posterior dependence between the target and background state, the predicted background statistics are used to construct approximate target-present and target-absent likelihoods for the Bernoulli filter. Evaluations using measurements generated from the statistical model and BELLHOP show improved target-confirmation and localization performance over constant-false-alarm-rate-based tracking. These results indicate that explicitly accounting for the background through a computationally tractable approximate likelihood can exploit weak target information without requiring full joint target–background inference.

Following a target speaker in a noisy environment, commonly known as the cocktail party problem, remains particularly challenging for cochlear implant (CI) users. Recent studies have explored EEG-based auditory attention decoding (AAD) using neural networks to enhance hearing assistance. This paper presents a resource-efficient ASIC for real-time EEG-based auditory attention decoding by integrating a quantized CNN inference engine and a Pearson-correlation classifier. The proposed architecture employs streaming execution, on-chip buffering, and memory-efficient dataflow to reduce hardware cost while maintaining real-time performance. The proposed ASIC has been fully implemented in GF22FDX 22-nm CMOS technology, occupying a total silicon area of 2.09 mm$^2$(1264$\mu$m x 1654$\mu$m), with the CNN inference engine and streaming classification engine requiring only 0.076 mm$^2$. Operating at a core voltage of 0.55 V, the design achieves a power consumption of 0.4941 mW and an inference latency of 7.34 ms, providing an energy-efficient hardware platform for EEG-based auditory attention decoding in hearing-assistance applications.

Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain incompletely specified. This paper proposes a standardization-oriented framework that treats evaluation design as part of the scientific contribution. It defines seven required study dimensions: protection objective, physical scope, observability, timing and decision windows, targets and sample validity, validation protocol, and evaluation outputs. The framework is instantiated in a bounded case study on the public PROTECT-90 electromagnetic-transient benchmark, comprising 9022 simulated episodes from a 90 kV double-line topology, for onset-conditioned fault classification and localization. Under centralized sensing, simulation-metadata-aligned 20 ms windows, and episode-grouped validation, a multi-layer perceptron (MLP) achieved a five-fold mean macro-averaged F1 score of 0.991 +/- 0.001 for classification and a localization mean absolute error of 10.20 +/- 0.25% of line length (mean +/- std across episode-grouped folds). Extending the decision horizon to 50 ms preserved this task-dependent performance asymmetry, while reduced observability approximately doubled the MLP localization error but had little effect on classification. A synchronized two-ended conventional locator outperformed the learning locators under its richer clean information set, and measurement degradation showed that clean predictive performance did not determine robustness. The framework turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions.

The demand for higher speeds in intra-data center interconnects will eventually require high-order pulse amplitude modulation (PAM) combined with soft-decision (SD) forward error correction (FEC). The laser relative intensity noise (RIN) is an important noise impairment that limits the performance of high-speed intensity-modulation (IM) and direct-detection (DD) systems, as it induces a channel with signal-dependent noise. In this paper, we show that an accurate calculation of the log-likelihood ratios (LLRs) is critical for the performance of the SD-FEC decoder in RIN-dominated IM-DD systems. First, we show that assuming signal-independent additive white Gaussian noise (AWGN) statistics to compute LLRs results in a significant penalty in performance. As an alternative, we propose a low-complexity piecewise linear approximation of the exact LLRs of Gray-coded PAM-4 and PAM-8 for the RIN-dominated IM-DD channel. We show using the generalized mutual information that our approximation results in no performance loss versus using exact LLRs, unlike the AWGN-like assumption. Furthermore, we validate the analysis with bit error rate (BER) performance of SD decoding. We show that our approximation matches the BERs achieved using exact LLRs. Therefore, our approximation avoids a BER error floor penalty of up to 7.2 times using an extended Hamming code with PAM-4, and up to 2.41 dB of optical modulation amplitude penalty using high-code-rate low-density parity-check codes with PAM-8 for the same target BER.

In movable antenna (MA) systems, antenna movement induces Doppler frequency shifts that are conventionally treated as an impairment requiring mitigation. In this paper, we propose Velocity Index Modulation for Movable Antennas (VIM-MA), which reframes this Doppler effect as an additional information-bearing degree of freedom. The transmitter selects the antenna movement velocity from a pre-designed discrete codebook, so that the resulting Doppler shift conveys extra index bits beyond those carried by the conventional modulation symbol. Codebook design is formulated as a spectral efficiency maximization over the velocity spacing $\delta$ and codebook size $N_v$, subject to an average-information Cramér–Rao-type bound (AIF-CRB) on velocity estimation accuracy, a physical track length constraint, and a spatial channel decorrelation constraint. A logarithmic change of variables renders the problem convex and yields a closed-form solution. We further establish that the peak codebook velocity equals $D_{\max}/T_s$, and that the decorrelation-limited spacing always lies below the Rayleigh Doppler resolution, so that VIM-MA is intrinsically a super-resolution scheme. A covariance-matched detector is derived that requires neither per-path angle knowledge nor channel state information. Simulation results show that the decorrelation-limited codebook, which carries five index bits over a $10\lambda$ aperture, is attainable only with oracle angle knowledge, whereas the channel-state-free detector is limited to three bits but reaches that payload approximately $10$~dB earlier than position-domain indexing charged a realistic pilot budget.

The accuracy and noise immunity of GNSS receivers are largely determined by the performance of their tracking modules. To optimize performance, designers should take into account several key issues: the choice of integration intervals, the time delay (spacing) between the early and late correlator channels, and the parameters of tracking loop filters. This article presents the results of an analysis of these important practical aspects. The obtained results display some ways of improv-ing the parameters of transient processes, which occur in tracking modules. The study was conducted on an experimental navigation receiver, processing GPS L1 signals. Experimental data were obtained using the simulation software package, developed by the authors, Xilinx environment Vivado, as well as the processing of recordings of real navigation signals obtained in full-scale tests.

Radio frequency (RF) fingerprinting authenticates wireless devices from hardware-induced I/Q impairments, typically with deep learning feature extractors that are accurate but opaque, limiting their use in security critical settings. We propose Polar Monotonic Kolmogorov-Arnold Networks (Polar MKAN), a block partitioned monotonic encoder on polar inputs in which each latent dimension depends exclusively on magnitude or phase, yielding channel separation and monotone responses by construction. On a synthetic gain and carrier frequency offset (CFO) benchmark, Polar MKAN reaches 57.2 percent DCI Disentanglement versus at most 12.9 percent for unpartitioned baselines. We further evaluate the detection accuracy trade off on real data and the sensitivity to blind CFO compensation.