Open-set recognition (OSR) of low-probability-of-interception (LPI) radar signals is challenging in non-cooperative environments. Due to parameter variations and low signal-to-noise ratios (SNRs), LPI radar features often exhibit multimodal structures. This limits conventional single-center representations, leading to loose acceptance regions and increased false acceptance of unknown samples. To address this issue, this paper devises a feature-space multi-center framework for OSR of LPI radar signals. It represents each known class with multiple local prototypes and a shared within-class covariance matrix to capture complex intra-class distributions. A consistency-driven filtering mechanism calibrates the rejection boundary using validation data, while a class-level prototype configuration strategy adapts model complexity to heterogeneous class structures. Experiments on a simulated LPI radar signal dataset across a wide SNR range show that our solution outperforms several representative baseline approaches in terms of the area under the receiver operating characteristic curve (AUROC) and the false positive rate at a 95% true positive rate (FPR@95%TPR). At an SNR of −6 dB, the proposed method achieves an FPR@95%TPR of 19.09% and an AUROC of 92.11%, indicating effective discrimination under noise-dominated conditions. These results indicate the potential of the proposed method for handling complex intra-class structures in OSR of LPI radar signals under simulated non-cooperative conditions.

The space-air-ground integrated network (SAGIN) has garnered significant attention in recent years due to its capability to extend communication networks from terrestrial environments to near-ground and space contexts. The application of SAGIN enables to achieve a high-quality, multi-functional, and complex communication requirements, which are essential for sixth-generation communication systems. This paper presents a topology aware (TA) framework to leverage the topological structure in SAGIN to address the multi-functional communication challenge, particularly the integrated sensing, communication, and power transfer (ISCPT) problem. To take advantage of the topological structure, we initially establish the topology according to the criteria of visibility and channel strength. The ISCPT problem can be reformulated into a topological structure as a mixed integer linear program, providing valuable insights from the objectives and constraints. Results demonstrate the superior performance of our solution compared to the benchmarks.

This paper presents a novel privacy-preserving distributed adaptive filtering framework designed to address both internal and external privacy threats in networked systems. Two algorithms, namely, IPAS-DLMS1 and IPAS-DLMS2, are developed, which incorporate two key innovations: (i) input perturbation with noise variance scaled to signal power, and (ii) dynamic amplitude modification of shared data. Unlike conventional differential privacy (DP) methods, our approaches enable the steady-state error to decrease with smaller step-sizes. Our theoretical analysis establishes the stability under convex and strongly-convex cost functions, and quantifies the impact of step-size, network topology, and system noise characteristics. Moreover, to improve robustness against large target weight norms, a weight energy normalization mechanism is introduced. Finally, simulation results demonstrate that the proposed algorithms outperform state-of-the-art DP-based methods in both accuracy and privacy preservation.

A common assumption in matrix completion (MC) and tensor completion (TC) is that the missing locations are sampled randomly. However, in real-world scenarios, the unobserved elements are often not arbitrarily located, and may concentrate within entire rows or columns. We refer to this missing mechanism as structural missingness, and traditional MC and TC schemes suffer from drastic degradation under these circumstances. This work addresses the challenge of restoring structural missingness by introducing a novel framework for simultaneously reconstructing multiple matrices, called multi-matrix completion (MMC). In MMC, tri-factorization across matrices captures the correlation between matrices, and Tikhonov regularization on each matrix exploits its correlation. This design enables MMC to efficiently handle both random and structural missingness. In addition, MMC is not affected by the smoothness along matrices which makes it suitable for a wider variety of data compared to Fourier transform based TC methods. The alternating direction method of multipliers is utilized to solve the resultant optimization problem. The global convergence of the algorithm is supported by comprehensive theoretical analyses. We demonstrate the versatility of MMC through extensive experiments in image and video restoration, and showcase its superior performance in comparison to traditional MC and TC methods.

Mobile crowdsensing (MCS) leverages distributed devices to collect and share real-time data for location-driven applications, yet its performance is constrained by the absence of continuous-time theoretical frameworks addressing worker mobility and energy efficiency. To our knowledge, there is no continuous-time framework yet that addresses both worker mobility patterns and power control strategies for MCS performance. This paper proposes a novel analytical framework for fog-assisted MCS in location-driven scenarios, adopting a continuous-time and spatiotemporal perspective. Two mobility models are employed: two-dimensional Brownian motion and constrained Brownian motion, to characterize the physical locations of MCS workers under realistic pathway constraints. Assuming time-uncorrelated Rayleigh fading, we evaluate key performance indicators, including departure time, instantaneous capacity, fog-aware temporal average transmission success probability (TATSP), cumulative sensing data and region of interest (ROI)-capacity. Furthermore, we assess the power consumption and energy efficiency of the path-loss inversion power control strategy in fog-aided MCS system. Our findings offer a comprehensive perspective on the spatiotemporal evolution of worker performance, establishing foundational principles for task allocation in MCS with unpredictable mobility patterns. Theoretical results suggests that long-term sensing tasks may be preferentially assigned to low-mobility workers to optimize fog-aware TATSP. Moreover, the energy efficiency decreases monotonically with respect to the initial link distance. While numerical results, validated by Monte Carlo simulations and real-world GPS trajectory data from the Geolife dataset, illustrate how mobility, initial link distance, and path-loss coefficient influence instantaneous capacity, ROI-capacity, cumulative sensing data and energy efficiency. It is also revealed that, assigning the tasks to the nearest low-mobility workers presents a simple yet effective scheme.

Single-bit low-rank matrix recovery (LRMR) aims to reconstruct a low-rank matrix using the sign information of the received data. Unlike conventional LRMR, it enables hardware systems to exploit single-bit analog-to-digital converters (ADCs) for quantization, significantly reducing cost and hardware complexity. However, the single-bit ADC obscures the distribution of additive noise, presenting a challenge in mitigating noise effect. In this work, we propose a novel quantization model for single-bit LRMR that transfers the additive noise from within the quantization function to the outside. Accordingly, the postquantization noise is characterized and its sparse discrete-valued property is revealed. Subsequently, we integrate this model with the matrix decomposition technique to formulate the single-bit LRMR problem. To effectively handle the noise, we adopt the truncated least squares (TLS) loss function, which offers greater robustness compared to the Frobenius norm. Given that the TLS function is non-convex and non-smooth, we employ half-quadratic optimization to simplify the associated intractable problem. Additionally, the proximal block coordinate descent method is adopted as the solver. Furthermore, we prove that the objective function value and variable sequence of the developed algorithm are convergent. Simulation results show that the proposed algorithm outperforms existing methods, including the applications of covariance matrix estimation and beamforming.

In sixth-generation and beyond, space-air-ground integrated networks (SAGINs) extend network connectivity to space, thereby enabling broader service coverage. This paper proposes a topology-aware SAGIN framework to address the integrated sensing, communication, and wireless power transfer (ISCPT) problem, leveraging the distinctive visibility of satellite-terrestrial and satellite-satellite users as well as their constructing in-between channel strengths. By modeling the topology of the SAGIN as a bipartite graph, we formulate the ISCPT problem as a multi-objective joint optimization problem with specified topological structures to reflect connection relationships of satellite-terrestrial and satellite-satellite users. The ISCPT problem is then reformulated and carefully decomposed as several mixed-integer linear programs (MILPs) by leveraging the network topology to individually optimize sensing, communication, and power transfer. To reduce the computational complexity of the proposed method, a greedy algorithm deal with generalized multi-assignment problem (GMAP) is developed. Simulation results demonstrate superior performance in communication and sensing, with a tolerable trade-off in wireless power transfer.

To address the limitations of existing information geometry based anti-deceptive jamming approaches for multistatic radar, such as the suboptimal exploitation of manifold structures and the high sensitivity of discrimination thresholds to multiple parameters, this study presents an enhanced data-level algorithm tailored for multi-target scenarios. Target measurements across coherent processing intervals are formulated as Hermitian positive definite matrices residing on a Riemannian manifold. To better quantify geometric discrepancies between physical and deceptive targets, we introduce a total low-rank nuclear norm divergence measure that jointly captures local and global structural variations. Building upon this divergence measure, a three-stage coarse screening–normalization–fine clustering framework is developed to suppress the effects of deceptive distance and measurement uncertainty. Simulation results indicate that, at short deception distances, the proposed method maintains approximately 99% physical target detection probability and significantly improves false target recognition performance, demonstrating its robustness and effectiveness under prior-free conditions.

Terahertz (THz) communication combined with ultra-massive multiple-input multiple-output (UM-MIMO) technology is promising for 6G wireless systems, where fast and precise direction-of-arrival (DOA) estimation is crucial for effective beamforming. However, finding DOAs in THz UM-MIMO systems faces significant challenges: while reducing hardware complexity, the hybrid analog-digital (HAD) architecture introduces inherent difficulties in spatial information acquisition the large-scale antenna array causes significant deviations in eigenvalue decomposition results; and conventional two-dimensional DOA estimation methods incur prohibitively high computational overhead, hindering fast and accurate realization. To address these challenges, we propose a hybrid dynamic subarray (HDS) architecture that strategically divides antenna elements into subarrays, ensuring phase differences between subarrays correlate exclusively with single-dimensional DOAs. Leveraging this architectural innovation, we develop two efficient algorithms for DOA estimation: a reduced-dimension MUSIC (RD-MUSIC) algorithm that enables fast processing by correcting large-scale array estimation bias, and an improved version that further accelerates estimation by exploiting THz channel sparsity to obtain initial closed-form solutions through specialized two-RF-chain configuration. Furthermore, we develop a theoretical framework through Cramér-Rao lower bound analysis, providing fundamental insights for different HDS configurations. Extensive simulations demonstrate that our solution achieves both superior estimation accuracy and computational efficiency, making it particularly suitable for practical THz UM-MIMO systems.

Federated learning distributes data among n clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tackle this issue, centered clipping and Huber aggregators have been exploited for Byzantine robustness. In this paper, we first demonstrate their equivalence via convex conjugate theory, and show that they can yield biased solutions in the presence of outliers, leading to failure under high data heterogeneity and a substantial fraction of outliers. Next, we propose a new robust aggregation rule that utilizes the truncated-quadratic (TQ) loss, effectively mitigating the biases of existing methods, such as centered clipping and Huber aggregators. We show that our aggregator achieves order-optimal Byzantine-robust learning under nonconvex loss functions and heterogeneous data, ultimately enhancing the reliability of federated learning systems. Additionally, we provide a robust deviation estimation strategy for TQ, demonstrating its effectiveness. Furthermore, we show that TQ maintains robustness even when only an estimate of the number of Byzantine clients is available. Finally, experimental results on MNIST, Fashion-MNIST, and CIFAR-10, indicate that our aggregator provides better robustness performance than the competing techniques.

A harmonic amplitude-based method for direction-of-arrival (DOA) estimation using time-modulated arrays (TMAs) is presented. In the proposed scheme, DOAs are estimated from a limited number of harmonic amplitudes without requiring harmonic phase information, thereby allowing asynchronous sampling between the radio frequency (RF) front end and the analog-to-digital converter (ADC). The received signals are modulated through multi-phase schemes with fixed delays, and the desired harmonic amplitudes are extracted via a zoom fast Fourier transform (FFT). The mathematical relationship between the amplitude ratio and the incident angle is derived, where the ratio is jointly determined by the time delay, baseline spacing, and harmonic order. A specific amplitude ratio, defined as the ratio of amplitudes at different harmonic orders, may correspond to multiple angles, leading to ambiguity. Under certain delay conditions, this mapping simplifies to a one-to-two correspondence, which is resolved by exploiting two sets of effective amplitude ratios to obtain common angle solutions. Simulation results demonstrate that the proposed approach achieves accurate DOA estimation and maintains robustness under synchronization errors. Experimental validation in the S-band confirms the effectiveness of the method.

As an approach to improve the uniform degrees-of-freedom (uDOFs) of sparse linear arrays, array motion, combined with the array dilation method, has received increasing attention, which increase the maximum uDOFs by several times through multiple uniform shifts and synthesis from array motion. In this work, an novel array dilation scheme adapted to arbitrary asymmetrically defined r-th-order cumulants with multiple shifts is proposed. By increasing the cumulants order or using longer shift sequences, a much higher number of uDOFs is achieved. Simulation results demonstrate the effectiveness of the proposed array dilation scheme.

Prior studies on mixed near-field and far-field communications have focused exclusively on single-cell scenarios, where both near-field and far-field users are served by the same base station (BS), leading to intra-cell mixed-field interference. In this paper, we consider a more general and practical multi-cell mixed-field scenario consisting of multiple cells, each serving multiple users, thus resulting in more complex inter-cell mixed-field interference. To address this new challenge, we propose leveraging rotatable antenna (RA) technology to enhance multi-cell mixed-field communication performance by exploiting the additional spatial degree-of-freedom (DoF) introduced by RA rotation to mitigate interference in an efficient way. Specifically, we study an RA-enabled multi-cell mixed-field communication system in which each BS is equipped with an RA array to serve its associated users. We formulate a network-wide sum-rate maximization problem that jointly optimizes the transmit beamforming and the rotation angles of the RA arrays, subject to per-BS power constraints and admissible array rotation limits. To gain useful insights into the role of RAs in multi-cell mixed-field communications, we first analyze a special case with a single user per cell. For this case, we obtain a closed-form expression for the rotation-aware inter-cell mixed-field interference using the Fresnel integrals and analytically show that RA rotation can effectively mitigate such interference, thereby substantially improving system performance. For the general case with multiple users per cell, we develop an efficient double-layer algorithm: the inner layer optimizes the transmit beamforming at each BS via semidefinite relaxation (SDR) and successive convex approximation (SCA); while the outer layer determines the rotation angles of the RA arrays using particle swarm optimization (PSO). Numerical results demonstrate that RA-enabled multi-cell systems achieve significant performance gains over conventional fixed-antenna systems, and the proposed joint design consistently outperforms various benchmark schemes.

Received signal strength (RSS) has been extensively studied for localization purposes, and the distributed nature of cell-free massive multiple-input multiple-output (CF-mMIMO) systems offers a new synergistic avenue for achieving high-precision localization. In this work, Open RAN and software-defined radio are used to realize the central and distributed units of a CF-mMIMO system to acquire the RSS measurements. By analyzing the experimental data, it is revealed that the RSS measured from the first-order reflection path can yield a sufficiently high signal-to-noise ratio for localization, enabling localization even without line-of-sight (LoS) paths. Inspired by this finding, a hybrid localization scheme, that can attain the accuracy benchmarked by Cram $\acute {\text {e}}$ r-Rao lower bound, is proposed to estimate the target position using both LoS and first-order non-line-of-sight RSS measurements. Furthermore, a theoretical framework is established to assess the fundamental limits of RSS-based localization in CF-mMIMO systems, offering a principled guideline for system designers to deploy and design localization systems in real-world scenarios.

The escalating scale of Large Language Models (LLMs) necessitates efficient adaptation techniques. Model merging has gained prominence for its efficiency and controllability. However, existing merging techniques typically serve as post-hoc refinements or focus on mitigating task interference, often failing to capture the dynamic optimization benefits of supervised fine-tuning (SFT). In this work, we propose Streaming Merging, an innovative model updating paradigm that conceptualizes merging as an iterative optimization process. Central to this paradigm is ARM (Activation-guided Rotation-aware Merging), a strategy designed to approximate gradient descent dynamics. By treating merging coefficients as learning rates and deriving rotation vectors from activation subspaces, ARM effectively steers parameter updates along data-driven trajectories. Unlike conventional linear interpolation, ARM aligns semantic subspaces to preserve the geometric structure of high-dimensional parameter evolution. Remarkably, ARM requires only early SFT checkpoints and, through iterative merging, surpasses the fully converged SFT model. Experimental results across model scales (1.7B to 14B) and diverse domains (e.g., math, code) demonstrate that ARM can transcend converged checkpoints. Extensive experiments show that ARM provides a scalable and lightweight framework for efficient model adaptation.

Direct localization (DL) of high-order quadrature amplitude modulation (QAM) sources is a pivotal challenge in wireless communications, particularly in environments characterized by complex multipath propagation and the presence of multiple sensor array-based anchors. This paper introduces a novel solution based on dual atomic norm minimization (DANM) framework that capitalizes on the fourth-order cumulant property of QAM signals to suppress Gaussian noise and expand the effective array aperture. Unlike traditional DL frameworks based on discrete Fourier transform (DFT) and spatial smoothing pre-processing (SSP) techniques, the proposed framework enhances localization accuracy and improves robustness against multipath effects. By framing the localization problem as a semidefinite program that utilizes dual atomic norm properties, our solution eliminates the need for prior knowledge of the number of sources and achieves a favorable balance between computational complexity and localization performance. Simulation results reveal that the DANM-based DL algorithm outperforms existing DFT- and SSP-based DL methods in terms of localization accuracy, with its root mean square error (RMSE) closely approaching the Cram & eacute;r-Rao bound (CRB) even under challenging conditions. These findings underscore the potential of DANM in advancing high-precision DL for high-order QAM sources, thereby paving the way for more reliable and precise wireless communication systems.

High-mobility scenarios have increasingly attracted attention due to their relevance in modern wireless communication systems, where doubly selective fading (DSF) significantly degrades transmission reliability. To address this issue, we propose a transceiver that combines orthogonal time frequency space (OTFS) modulation with differential chaos shift keying (DCSK) to provide robustness against DSF. In this design, mapping symbols onto the OTFS delay-Doppler (DD) grid exploits DD diversity, delivering high reliability under DSF and achieving markedly lower bit error rate than conventional schemes. To fully realize these advantages, this paper devises an alternating direction method of multipliers (ADMM)-based signal detection method tailored specifically for OTFS-DCSK. Our solution employs the least squares QR (LSQR) algorithm to leverage channel sparsity, along with truncated singular value decomposition (SVD) to exploit the rank-1 structure of the transmitted symbols, achieving effective noise reduction. Both LSQR and SVD exhibit acceptable complexity, resulting in a computationally efficient detector. Simulation results verify that the developed ADMM algorithm significantly improves reliability, yielding lower normalized mean squared error and bit error rate compared to existing approaches under DSF channels.

The graph least mean square algorithm performs poorly in restoring the graph signal when the sampled observations are disturbed by impulsive noise. To address this problem, a new robust adaptive graph algorithm based on the half quadratic criterion is derived in this paper. As a parameter involved in the loss function governing the algorithm performance, an adaptive scheme that uses the sampled error signals is devised to adjust it properly. Furthermore, a variable step-size strategy is suggested to achieve fast convergence during the transient state and low steady-state error upon convergence. Stochastic behavior of the developed algorithm is analyzed. Lastly, extensive simulations are conducted to demonstrate the superiority of our approach over competing methods for both fixed and time-varying graph signals in outlier-contaminated environments.

Large-scale multiple-input-multiple-output systems extend the Fresnel region, transforming the target source type from far-field to near-field (NF) with curved spherical wavefront, which poses challenges for accurate target localization. In this article, an exact NF polarization localization algorithm is proposed to estimate the 5-D parameters by establishing an L-shaped cocentered orthogonal loop and dipole (COLD) array. First, estimation of manifold matrices aligned with each coordinate axis is performed by forming the delay cross-correlation covariance matrices, and employing parallel factor decomposition. The unambiguous phases present in each submanifold matrix are employed to disambiguate the remaining phases, enabling the construction of linear equations in spatial geometry, from which linear least-squares parameter estimation is allowed. In particular, based on the rotational invariance relationship between the horizontal and vertical components received by the COLD array, the polarization parameters are estimated via total least squares. Under the exact NF polarization scenario, we derive the closed-form asymptotic parameter variances and the corresponding Cram & eacute;r-Rao bound as benchmark. The proposed algorithm operates without phase approximation and is free from quarter-wavelength sensor spacing limitation, and extensive simulations are provided to demonstrate its superiority over competing schemes in terms of parameter estimation performance.

High-precision direction-of-arrival (DOA) estimation, as a key sensing capability for 6G-enabled applications such as autonomous driving and extended reality, is increasingly dependent on the effective exploitation of spatial degrees of freedom (DOFs). This paper integrates two frontier DOFs-oriented paradigms and proposes a fluid antenna-enabled hybrid analog-digital (FA-HAD) architecture, which features an extremely lightweight front-end configuration mechanism and efficient spatial DOFs exploitation. Within this architecture, a collaborative spatial-phase sampling strategy is first developed to enable real-time 2-D DOA estimation under compressive observations, and a single-source CRLB analysis is provided to quantify the achievable performance limit, offering quantitative guidance for accuracy-overhead trade-offs. Furthermore, an efficient virtual-array spatial covariance matrix reconstruction method is proposed to recover a physically meaningful covariance representation, thereby providing a covariance-domain interface that is directly reusable by a broad class of existing covariance-based array processing and array design techniques, which strengthens the scalability and transferability of the proposed architecture. Building upon the reconstructed SCM, a Jacobi-Anger expansion based dimension-reduced MUSIC estimator is further derived for arbitrary planar arrays with a favorable computational cost. Simulation results demonstrate that the proposed FA-HAD framework attains DOA accuracy close to fully digital systems while substantially reducing RF hardware complexity and training overhead.