Deceptive jamming poses a significant threat to synthetic aperture radar (SAR) systems due to its low power requirements, flexibility, and high-fidelity false target generation capabilities. Existing waveform-agility-based countermeasures often rely on the critical assumption that jammers cannot estimate the transmitted signal parameters within the current pulse repetition interval due to digital radio frequency memory processing latency. However, advancements in jammer technologies challenge this assumption, potentially compromising the effectiveness of these countermeasures. To address this issue, we propose an amplitude-dithered waveform technique that eliminates dependence on jammer delay assumptions. Specifically, the transmitted signal employs randomized amplitude dithering in the time domain, which prevents jammers from accurately estimating the signal amplitude and thereby reduces coherence between the jamming and echo signals. The proposed approach significantly mitigates sensitivity to signal orthogonality constraints, enhancing deceptive jamming suppression performance against advanced real-time deceptive jamming. Furthermore, we provide a comprehensive theoretical framework by deriving the ambiguity and cross-ambiguity functions for the proposed schemes. The effectiveness of our approach is validated through extensive simulations, demonstrating superior jamming suppression performance in both point target scenarios and complex scene imaging environments. The experimental results align closely with theoretical predictions, further confirming the practical viability of our approach for robust SAR imaging in hostile environments.

Flexible intelligent metasurface (FIM) technology has emerged as a promising technology for enhancing wireless communication performance by dynamically reshaping the propagation environment. Compared with conventional rigid reconfigurable intelligent surfaces (RIS), an FIM is composed of multiple electromagnetic (EM) scattering units, each of which can flexibly modify its displacement in the direction normal to the surface, thereby cooperatively morphing the overall surface shape. This additional degree of freedom (DoF) enables improved beamforming and interference mitigation, particularly in complex multicell scenarios. In this paper, an optimization problem for maximizing the weighted sum-rate (WSR) in a multicell multi-user multiple-input single-output (MU-MISO) system assisted by an FIM deployed at the cell boundary is investigated. We jointly optimize the transmit beamforming at the base station (BS), the phase shift matrix, and the FIM surface shape, subject to constraints on the transmit power budget, unit-modulus reflection coefficients, and surface shape morphing range. Due to the non-convex objective function with highly coupled variables, solving the formulated optimization problem is challenging. To tackle this challenge, we propose an efficient alternating optimization framework that leverages the weighted minimum mean square error (WMMSE) method to reformulate the problem and the block coordinate descent (BCD) algorithm to iteratively update the variables. Specifically, the Riemannian conjugate gradient (RCG) algorithm is leveraged to optimize the phase shift matrix, while the projected gradient descent (PGD) method is adopted to optimize the surface shape of the FIM. Additionally, the optimal beamforming vectors are obtained in closed form. Finally, our simulation results demonstrate that the FIM-assisted system achieves an average 33% improvement in WSR across various scenarios, outperforming conventional RIS schemes.

Reconfigurable intelligent surfaces (RISs) can improve radar target detection by introducing additional controllable propagation paths. In practice, the distributed placement of multiple RISs leads to asynchronous propagation, resulting in path-dependent delay offsets that can degrade the performance of conventional single-matched-filter (SMF) detectors. This work develops a signal model that accounts for such delays and proposes a multi-matched-filter (MMF) detection framework, in which each path is processed by a dedicated MF. The approach also includes joint optimization of the transmit beamformer and RIS phase shifts to maximize detection probability. Analytical detection probability expressions are derived for the asynchronous model and verified via Monte Carlo simulations. Simulations show that both MMF and SMF detectors benefit from RIS deployment compared to systems without RISs. The results further indicate that, as the number of RISs increases, MMF achieves greater detection performance improvements than SMF, particularly under asynchronous conditions.

This paper addresses the problem of spectrum sensing using multi-antenna cognitive receivers in unknown heteroscedastic noise environment, where the noise variances may vary in space and time. Specifically, we propose a robust data-driven spectrum sensing approach using a covariance-based deep convolutional neural network (CNN). In particular, we take the sample covariance matrix (SCM) with its unknown noise variances being well suppressed as the input of CNN to train a robust and generalized test statistic against the heteroscedastic noise. Meanwhile, we design a CNN architecture with a strided convolution layer to retain detailed feature information of the noise-suppressed SCM and a batch normalization layer to accelerate the CNN training. Various simulation results demonstrate that the proposed method attains an accurate detection performance and adapts well to different types of heteroscedastic noise. Particularly, the proposed approach achieves detection probabilities exceeding 99% and 95% under worst noise power ratios of 5 and 80, respectively, when the signal-to-noise ratio is-18 dB with a false alarm probability of 10%.

This letter investigates the problem of robust target localization in distributed multiple-input multiple-output (MIMO) radar systems under space-time imperfections, i.e., antenna position errors and clock biases at both transmitters and receivers, using bistatic-range (BR) measurements. The space-time errors and BR measurement noise are characterized by hierarchical Gaussian distributions with Gamma hyper-priors imposed on their precision parameters. We develop a computationally efficient approximate belief propagation algorithm to resolve the intractable loopy Bayesian inference problem. In particular, the BR-related and precision-related messages are handled through local linearization and Gaussian moment-matching projection. This enables fully Bayesian adaptive inference without exact prior variances of the space-time errors and noise. Simulation results under various conditions demonstrate the effectiveness of the proposed algorithm.

Most existing target sensing approaches in integrated sensing and communication (ISAC) systems assume a regular time-frequency resource allocation. However, in practical ISAC systems, resources are often allocated irregularly because of the randomness of user scheduling. This paper addresses such resource-irregular scenarios by integrating the CANDECOMP/PARAFAC decomposition (CPD) framework with tensor completion. The proposed structured tensor completion and decomposition (STCD) method enhances target sensing by not only processing echo signals from irregularly allocated resource regions but also interpolating those from unallocated ones. Moreover, tensor completion reconstructs the Vandermonde structure of steering matrices. By enforcing a tensor rank-1 constraint, the STCD method leverages the Vandermonde structure to establish more relaxed uniqueness conditions for CPD compared with existing approaches. Additionally, we present the Cram & eacute;r-Rao bound results for STCD in angle-range-velocity estimation, extending prior analyses from resource-regular to resource-irregular scenarios. Simulation results validate the effectiveness of the proposed STCD method for resource-irregular target sensing, demonstrating improved performance over traditional methods and its unstructured counterpart.

We consider the channel acquisition problem for a wideband terahertz (THz) communication system, where an extremely large-scale array is deployed to mitigate severe path attenuation. In channel modeling, we account for both the spherical wavefront and beam-splitting phenomena of the wide-band near-field channel. We propose a frequency-independent orthogonal dictionary that generalizes the standard discrete Fourier transform (DFT) matrix by introducing an additional parameter to capture near-field effects. This dictionary enables an efficient two-dimensional (2D) block-sparse representation of the wideband near-field channel. By leveraging this structured sparsity, the wideband near-field channel estimation problem can be effectively solved within a customized compressive sensing framework. Numerical results demonstrate the significant advantages of our proposed 2D block-sparsity-aware method over conventional polar-domain-based approaches for near-field wideband channel estimation.

Channel estimation is essential to massive multiple-input multiple-output (MIMO) systems. While recent generative model-based approaches using lightweight diffusion models (DMs) have achieved superior performance, they typically rely on a single data-driven prior, which limits their adaptability to varying channel distributions in real-world scenarios. To address this deficiency, we propose a mixture-of-experts (MoE) diffusion model (DM) framework combined with variational Bayesian inference. Specifically, our approach employs multiple pre-trained DMs, with each trained on a specific type of propagation channels. We then propose a probabilistic graphical model in which the channel is modeled as a latent variable drawn from one of these candidate generative priors with a certain probability. By integrating variational Bayesian inference with DM-based data priors, the underlying channel along with the expert indicator variable are jointly inferred, thus enabling automatic model adaptation for channel estimation. The effectiveness of our approach is evaluated on 3GPP CDL channels. Simulation results demonstrate that our proposed approach achieves a clear performance improvement over the standard DM-based method that employs a single prior trained on aggregated data from all channel types, particularly when the channel samples from different propagation environments are imbalanced.

In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on the first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method. methods.

Networked integrated sensing and communication (ISAC) offers significant potential for next-generation wireless systems. By exploiting spatial diversity through the cooperation of multiple base stations (BSs), this architecture expands coverage and achieves enhanced sensing performance. However, accurate sensing in networked ISAC requires time-frequency synchronization among BSs. Existing synchronization methods for networked ISAC suffer from inter-path interference caused by sensing channel compression. To address this problem, this paper proposes a structured canonical polyadic decomposition (SCPD) algorithm that effectively separates the multipath components of the sensing channel. Benefiting from this separation, SCPD achieves joint network-level synchronization and multi-target parameter estimation. We establish theoretical identifiability conditions for SCPD and show that it asymptotically achieves the Cramér-Rao bound. Furthermore, by incorporating parameters estimated from different BS pairs, we propose a multi-target tracking algorithm designed for the continuous operation of the system. The proposed algorithm tracks both the trajectories and velocities of moving targets by leveraging geometric diversity. Utilizing tracking results from the previous snapshot, an adaptive beamforming scheme is also developed to improve tracking performance in the next snapshot. Simulation results demonstrate that the proposed algorithms achieve superior accuracy and outlier robustness for both synchronization and sensing in networked ISAC, outperforming traditional approaches.

This paper addresses the jamming detection problem in colored noise environments. Low-power attacks are particularly dangerous due to their stealthiness and persistent harmful effects. The fluctuations caused by colored noise backgrounds further exacerbate this issue, making it easier for these attacks to remain concealed within the noise. We apply a source enumerator, namely the second-order-difference (SOD) algorithm that is robust against both white and colored noise, for jamming detection, and the resulting method is referred to as the SOD jamming detection (SODJD) method. Our analysis of jamming power’s impact on SODJD performance demonstrates that while SODJD can address colored noise, it is limited in scenarios with low-power signals mixed with several higher-power signals. To overcome these limitations, we propose an invariant SOD jamming detection (ISODJD) method by redesigning the criterion rule and introducing an adjustable parameter. Further analysis reveals that larger parameter values lead to improved detection performance, and the increased false alarm rate can be mitigated in massive channel systems, making ISODJD suitable for cooperative wireless communication scenarios. We implement our schemes and conduct extensive performance comparisons. Simulations demonstrate the effectiveness and superiority of the proposed method. Compared to the SODJD method, a 60-antenna MIMO system achieves a 4 dB improvement in detection sensitivity for low-power jamming signals under colored noise, aligning with our theoretical analysis.

In this paper, we study the problem of uplink channel estimation for near-filed orthogonal frequency division multiplexing (OFDM) systems, where a base station (BS), equipped with an extremely large-scale antenna array (ELAA), serves multiple users over the same time-frequency resource block. A non-orthogonal pilot transmission scheme is considered to accommodate a larger number of users that can be supported by ELAA systems without incurring an excessive amount of training overhead. To facilitate efficient multi-user channel estimation, we express the received signal as a third-order low-rank tensor, which admits a canonical polyadic decomposition (CPD) model for line-of-sight (LoS) scenarios and a block term decomposition (BTD) model for non-line-of-sight (NLoS) scenarios. An alternating least squares (ALS) algorithm and a non-linear least squares (NLS) algorithm are employed to perform CPD and BTD, respectively. Channel parameters are then efficiently extracted from the recovered factor matrices. By exploiting the geometry of the propagation paths in the estimated channel, users' positions can be precisely determined in LoS scenarios. Moreover, our uniqueness analysis shows that the proposed tensor-based joint multi-user channel estimation framework is effective even when the number of pilot symbols is much smaller than the number of users, revealing its potential in training overhead reduction. Simulation results demonstrate that the proposed method achieves markedly higher channel estimation accuracy than compressed sensing (CS)-based approaches.

Traditional multi-frame track-before-detect (MF-TBD) algorithms, originally designed for point-like targets, can enhance the detection and tracking of weak targets by integrating radar measurements over consecutive frames. However, with advances in radar resolution, the detection of range-spread targets (RSTs), whose energy is dispersed across unknown scattering centers (SCs), has become increasingly important. The MF-TBD algorithm applied to RSTs may suffer performance degradation due to the insufficient integration of target energy. We make two contributions to address this problem. First, we propose an MF-TBD algorithm tailored for RSTs (RST-MF-TBD) based on the multi-frame joint estimation of the target state sequence and the spatial distribution of SCs using the maximum likelihood (ML) criterion. Second, since the proposed RST-MF-TBD is a general procedure without specific model assumptions, we derive its explicit equations for the frequently encountered case of zero-mean Gaussian noise. Furthermore, we develop an efficient implementation of RST-MF-TBD by incorporating a sparse representation (SR)-based estimation strategy for the prior knowledge of target SCs, achieving both tractable computational complexity and superior detection performance. Finally, numerical simulations and real-measured data are used to validate the effectiveness of the proposed RST-MF-TBD algorithm across different target scattering models and in comparison with competitive methods.

Intelligent reflecting surface (IRS) is emerging as a transformative technology for next-generation wireless communication and sensing systems. In this letter, we consider the problem of adaptive beamforming (ABF) for a single-antenna receiver aided by a nearby IRS, where the receiver aims to extract signals from a desired direction in the presence of $K$ strong, unknown interferences. Specifically, we propose to maximize the signal-to-interference-plus-noise ratio (SINR) by optimizing the reflection coefficients of the IRS. Unlike conventional ABF methods, we do not have direct access to the signal-plus-interference covariance matrix. Instead, only a limited number of quadratic compressive measurements can be obtained. To close this gap, we present a sample-efficient analytical solution via implicit inference of the interference covariance matrix. Simulation results demonstrate that our method significantly improves the SINR over state-of-the-art approaches.

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology in wireless communications, enabling dynamic channel manipulation through economical hardware configurations to enhance network performance. However, optimizing their reflection coefficients still relies heavily on accurate channel state information (CSI), leading to high pilot overhead and increased system complexity. To overcome these limitations, we propose a probability update (PU)-based dynamic codebook framework for RIS-aided multiuser multiple-input single-output (MU-MISO) communication systems. By iteratively updating the probability distribution of RIS phase shifts according to the performance of previously selected reflection coefficients, the PU codebook scheme strikes a flexible trade-off between pilot overhead and system performance. Furthermore, we develop an adaptive probability update (APU) strategy that further reduces training overhead by dynamically adjusting the codebook size at each stage. Finally, numerical results illustrate that the proposed PU scheme effectively balances training overhead and system performance by relying only on composite end-to-end channel estimation, achieving comparable performance to CSI-based methods with significantly reduced pilot overhead, while the APU scheme further accelerates convergence and maintains robust performance under limited training overhead.

Distributed hybrid active-passive radars (HAPRs) with separately deployed transmitters and receivers face localization performance limitations due to the inevitable presence of direct-path interference (DPI). To improve target localization accuracy and enhance DPI suppression, we develop a direct position estimator tailored for stationary/low-speed targets in distributed HAPRs, which jointly exploits signals from both active transmitters and noncooperative illuminators of opportunity (IOs). However, the joint processing of active and passive observations, coupled with the unknown signals from noncooperative IOs, leads to an intractable high-dimensional optimization problem. The expectation-maximization algorithm is employed to address the optimization problem with low complexity, iteratively suppressing DPI and updating the position estimate. To further reduce computational complexity, we use a sequential estimation method that decomposes the high-dimensional problem into a sequence of low-dimensional ones. We also derive the Cramer-Rao lower bound (CRLB) to provide an accurate performance benchmark for the proposed estimator. Extensive numerical analysis shows that the proposed direct estimator achieves estimation accuracy closer to the CRLB than estimators used in only active or passive distributed radars.

In this paper, we present a secure beamforming method for near-field multi-user communication scenarios. In particular, the exact location of the eavesdropper is unknown but presumed to be within a potential region, as the base station cannot determine its accurate location. The objective is to maximize the worst case secure capacity across the entire potential region subject to the transmit power constraint. To address this challenge, we propose an alternating optimization (AO) algorithm. Specifically, we decouple the original multi-user joint optimization problem into multiple single-user feasibility problems, and then alternately design each user's beamfocusing vector. For each subproblem, we develop a single-point control method, enabling precise control of secure communication capacity at any spatial location to a specified desired value. By applying the proposed method to each point, we ensure that the entire region reaches the required secure capacity after iterations, thus obtaining a feasible solution for each subproblem. By employing binary search method, we can iteratively refine the secure capacity threshold and obtain the beamfocusing vectors that maximizes the secure capacity. Numerical results indicate that the proposed algorithm can effectively fulfill the requirements for near-field multi-user secure communication even when the eavesdropper and the users are located at the same angle with respect to the transmit antenna array. Moreover, the proposed algorithm offers advantages in computational efficiency and flexibility. When the location of the eavesdropper changes, the current beamformer can be readily adapted to meet new requirements without requiring a complete redesign.

This paper addresses the security issues in Radar-Communication Coexistence (RCC) systems, focusing on verifying the origin of the received signal through a tag-based Physical Layer Authentication (PLA) scheme. Traditional PLA schemes face challenges in RCC systems due to radar interference, which affects robustness against impersonation attacks, security against eavesdropping, and compatibility with source message reception. To address these challenges, it is necessary to redesign the optimal constellation of the source message and the tag for an RCC system utilizing a tag-based PLA scheme. We propose three tag-based PLA schemes for RCC systems: the Unified-Phase Tag (UPT) scheme, where all constellation points of a transmitted message share the same initial phase from a tag constellation set; the Individual-Phase Tag (IPT) scheme, where each constellation point of a transmitted message has different initial phases from tag constellation sets; and the Joint-Optimization Tag (JOT) scheme, where the phase of a tag for each constellation point of a transmitted message is jointly optimized with the signal and tag constellation sets. Our theoretical analysis of the proposed schemes covers compatibility, security, and robustness, with closed-form or upper-bound expressions for the symbol error rate derived. Extensive performance comparisons through simulations demonstrate that the theoretical results closely align with the simulation outcomes. For an RCC system with eighth order modulation and SNR = 16 dB under low radar interference to noise ratio scenario, the UPT, IPT, and JOT schemes show compatibility improvements of 18%, 19%, and 58%, respectively, compared to prior schemes.

Joint radar-communication systems, also known as integrated sensing and communication (ISAC) systems, have emerged as a promising solution for next-generation wireless networks. Existing ISAC-based target sensing methods primarily focus on regular time-frequency resource allocations, which, however, are not representative of practical scenarios due to the irregularity caused by random user scheduling. This paper addresses this challenge by proposing a structured tensor completion and decomposition (STCD) method for ISAC-based target sensing in resource-irregular scenarios. The STCD method handles irregular resource allocations by incorporating tensor completion to recover missing echo signals and leveraging the Vandermonde structure within the canonical polyadic decomposition. The Vandermonde structure is enforced as a tensor rank-one constraint in the STCD method through the Hankel transform. The resulting optimization problem is efficiently solved using an alternating least squares framework. Simulation results demonstrate the enhanced performance of the STCD method compared to conventional tensor-based and subspace-based approaches in angle-range-velocity sensing.

A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)--based hybrid detector from the composite probability distribution of the compound observations received by the fusion center, and then analyze its asymptotic detection performance. Subsequently, we optimize the sensor-wise quantization thresholds to achieve near-optimal asymptotic performance at the local sensor level. Moreover, we propose a mixed-integer linear programming approach to solve the optimization problem of transmission bandwidth allocation accounting for bandwidth constraints and error-prone channels. Finally, simulation results demonstrate the superiority of the proposed hybrid detector and the bandwidth allocation strategy, especially in challenging error-prone channel conditions.