Software defined radio (SDR) technology offers a programmable and reconfigurable foundation for custom vector network analyzer (VNA) platforms. Existing SDR-based VNA implementations, however, are often constrained by operating frequency range, sequential frequency-sweep speed, and insufficient characterization of measurement limitations. Motivated by these constraints, this article presents an automated SDR-based experimental platform for broadband transmission response (S21) characterization of passive radio frequency (RF) devices from 100 MHz to 7.2 GHz, implemented on a USRP X410 SDR. The platform uses a Zadoff-Chu (ZC) sequence with correlation processing to estimate the complex frequency response over one instantaneous sub-band from a broadband acquisition record, together with path selection controlled by RF switches and complex transmission response normalization. This approach replaces sequential point-by-point sweeping within one sub-band with a broadband correlation acquisition. However, the frequency spacing is still determined by the ZC sequence length and sampling rate, and random-noise reduction depends on the number of repeated ZC periods used for averaging. The present implementation is validated for passive device S21 measurements through comparison with a commercial VNA. It is not presented as a full substitute for a calibrated two-port VNA, because reflection measurements, full two-port error correction, metrology-grade traceability, and the dynamic range and long-term stability of commercial VNAs remain outside the demonstrated capability of this platform.

This paper investigates bistatic polarized Multi-Input Multi-Output (MIMO) radar for downward-looking detection in the presence of range-ambiguous clutter. While moderate-to-high Pulse Repetition Frequencies (PRFs) avoid Doppler ambiguity, they induce severe range ambiguity and strong clutter interference. To address this issue, an interpulse polarization-variation strategy is proposed. On the transmit side, a Variable Polarization Group (VPG) is defined according to the range-ambiguity number, assigning distinct polarization states to adjacent pulses to increase polarization Degrees of Freedom (DOF). On the receive side, polarization filtering is performed prior to Space-Time Adaptive Processing (STAP), enabling clutter suppression without increasing STAP computational complexity. A relaxation-based approach is further developed to efficiently compute the filtering vectors by converting the original nonconvex design into a semidefinite optimization problem. Numerical results illustrate the designed polarization vectors on the Poincaré sphere and demonstrate performance gains under different range-ambiguity conditions. Comparisons with state-of-the-art methods using Signal-to-Clutter-plus-Noise Ratio (SCNR) loss confirm the effectiveness, robustness, and fast convergence of the proposed method across different target polarization scattering matrices, polarization strategies, and optimization parameters. Moreover, we validate the capabilities of the proposed method under non-ideal conditions, including non-ideal polarization isolation, clutter temporal decorrelation, and spatial decorrelation.

Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range 1 (FR1) to Frequency Range 3 (FR3, 7-24 GHz). Realizing this vision faces two challenges. First, physics-based ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibitive for multi-band and multi-directional analysis over large areas. Second, current 5G systems rely on inter-frequency measurement gaps for carrier aggregation and beam management, which reduce throughput, increase latency, and scale poorly as bands and beams proliferate. These limitations motivate a data-driven approach to infer high-frequency characteristics from low-frequency observations. This work proposes CommUNext, a unified deep learning framework for cross-band, multi-directional signal strength (SS) prediction. The framework leverages low-frequency coverage data and crowd-aided partial measurements at the target band to generate high-fidelity FR3 predictions. Two complementary architectures are introduced: Full CommUNext, which substitutes costly RT simulations for large-scale offline modeling, and Partial CommUNext, which reconstructs incomplete low-frequency maps to mitigate measurement gaps in real-time operation. Experimental results show that CommUNext delivers accurate and robust high-frequency SS prediction even with sparse supervision, substantially reducing both simulation and measurement overhead.

This paper studies location privacy in uplink MIMO systems, where a user equipment seeks to spoof the angular signature observed by a single base station performing localization. We propose a blind analog precoder design that manipulates the perceived angle-of-arrival and angle-of-departure configuration without requiring channel-gain knowledge. The method enforces consistency between the received signal and a desired spoofed angular subspace, and is solved using an alternating optimization algorithm under practical amplitude constraints. Simulations in a multipath scenario show that the proposed approach achieves near-perfect angular spoofing and clearly outperforms pilot-only blind spoofing, which exhibits an error floor. The results also show a trade-off between spoofing accuracy and communication rate, depending on the chosen virtual geometry.

This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control. To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework that jointly optimizes the discrete RIS phase profiles and the UE's transmit power in real time. Specifically, our approach employs a hybrid training methodology integrating the neuroevolution paradigm with supervised learning, effectively overcoming the non-differentiability of discrete phase responses from the RIS unit elements and the strict information bottleneck of single-bit feedback messages for pilot power control. The proposed DA active sensing framework can be applied with both single- and multi-antenna BSs, the latter with only minor modifications in the structure of one NN: an additional output branch with appropriate structure is included for the latter case to select a valid digital combiner from a finite set. Extensive numerical simulations demonstrate that the proposed scheme achieves highly accurate and robust tracking across diverse target motion models, outperforming extended Kalman and particle filters, as well as, machine learning-based trackers. Furthermore, in static localization, it is shown to significantly outperform traditional fingerprinting schemes, deep reinforcement learning baselines, and standard backpropagation-based estimators.

Interrupted sampling repeater jamming (ISRJ) is a coherent jamming technique combining both deception and suppression capabilities, posing a serious threat to pulse-Doppler radar. Although existing studies have proposed various waveform design methods to counter the ISRJ, these approaches often struggle to balance jamming suppression performance and Doppler tolerance, limiting their practical application in high-speed target detection scenarios. To address this issue, a joint waveform and filter design method is proposed based on a time-domain masking principle. Precisely, the operational waveform and the operational filter adopt a linear frequency modulation (LFM) scheme to preserve Doppler tolerance, meeting the detection requirements for moving targets; whereas the masking waveform and the masking filter are designed using phase-coded sequences. An objective function is formulated by combining the jamming integration energy and the squared absolute deviation of the pulse compression peak gain from predefined thresholds. Further, a joint optimization framework based on Block Coordinate Descent (BCD) and Majorization-Minimization (MM) are employed to iteratively solve for the masking waveform and filter. In addition, the Lanczos method is introduced to reduce the computational burden of the proposed algorithm. Finally, simulation results demonstrate that the proposed joint design algorithm effectively suppresses ISRJ while maintaining satisfied Doppler tolerance. Compared with existing methods, the designed waveform and filter exhibit superior performance in both anti-jamming capability and Doppler adaptability.

Digital Radio-Frequency Memory (DRFM) jamming critically threatens modern radar systems by corrupting echo signals. We propose a robust, training-free algorithm that integrates three modules to reconstruct true echoes from contaminated spectrograms: skewness-adaptive spectral gating for distribution-aware energy segmentation, structure-preserving median smoothing for edge-robust denoising, and geometric refinement for orientation-aligned ridge consolidation. Simulations under extreme jamming-to-signal ratios (JSR) yield a mean Intersection-over-Union (mIoU) exceeding 0.8 at 30 dB. It also outperforms a state-of-the-art DeepLabv3 benchmark. Eliminating the need for training data, the proposed approach offers a practical solution in hostile environments.

To address the issue that 1-D high-resolution range profiles are susceptible to perspective variations and signal-to-noise ratio (SNR) fluctuations under single-radar condition, we propose a cooperative recognition framework for multiradar and multitemporal sequences. Principal component analysis compression and nearest-neighbor probabilistic discrimination are performed independently for each radar channel, producing per-radar posterior probability vectors for subsequent fusion. Multiradar recognition results are first subjected to instantaneous fusion and sequentially updated using recursive Bayesian methods, enabling evidence accumulation and probability calibration. Simulations demonstrate that the recognition accuracy stably increases with time steps and tends to converge. With per-radar reference SNRs of 3, 1, -1 and -3 dB, the weighted exponential product strategy, combined with instantaneous data fusion and recursive Bayesian sequential updates, forms a dual-fusion framework of multiradar and multitemporal sequences, thereby achieving leading performance, reaching a terminal accuracy of 86.83% and improving over the best single-radar baseline at the initial step (61.50%) by 25.33 percentage points. With per-radar reference SNRs of 20, 18, 16, and 14 dB, various methods exhibit comparable performance, but the multiradar scheme still maintains a robust advantage.

Integrated sensing and communication (ISAC) is a key technology for enabling a wide range of applications in future wireless systems. However, the sensing performance is often degraded by model mismatches caused by geometric errors (e.g., position and orientation) and hardware impairments (e.g., mutual coupling and amplifier non-linearity). This paper focuses on the angle estimation performance with antenna arrays and tackles the critical challenge of array beam pattern calibration for ISAC systems. To assess calibration quality from a sensing perspective, a novel performance metric that accounts for angle estimation error, rather than beam pattern similarity, is proposed and incorporated into a differentiable loss function. Additionally, a cooperative calibration framework is introduced, allowing multiple user equipments to iteratively optimize the beam pattern based on the proposed loss functions and local data, and collaboratively update global calibration parameters. The proposed models and algorithms are validated using real-world beam pattern measurements collected in an anechoic chamber. Experimental results show that the angle estimation error can be reduced from 1.01 degrees to 0.11 degrees in 2D calibration scenarios, and from 5.19 degrees to 0.86 degrees in 3D calibration scenarios.

Accurate cross-band channel prediction is essential for 6G networks, particularly in the upper mid-band (FR3, 7--24 GHz), where penetration loss and blockage are severe. Although ray tracing (RT) provides high-fidelity modeling, it remains computationally intensive, and high-frequency data acquisition is costly. To address these challenges, we propose CIR-UNext, a deep learning framework designed to predict 7 GHz channel impulse responses (CIRs) by leveraging abundant 3.5 GHz CIRs. The framework integrates an RT-based dataset pipeline with attention U-Net (AU-Net) variants for gain and phase prediction. The proposed AU-Net-Aux model achieves a median gain error of 0.58 dB and a phase prediction error of 0.27 rad on unseen complex environments. Furthermore, we extend CIR-UNext into a foundation model, Channel2ComMap, for throughput prediction in MIMO-OFDM systems, demonstrating superior performance compared with existing approaches. Overall, CIR-UNext provides an efficient and scalable solution for cross-band prediction, enabling applications such as localization, beam management, digital twins, and intelligent resource allocation in 6G networks.

High precision positioning is a key enabler for next-generation communication applications such as smart transportation and augmented reality. Reconfigurable intelligent surface (RIS) technology can enhance positioning by providing additional angular information and improving coverage under obstructed propagation conditions. However, true RIS beams can differ significantly from the simplified or ideal beam response models commonly used in RIS-aided positioning, leading to beam model mismatch and an elevated positioning error floor. This paper proposes an on-site RIS beam calibration framework that reduces this error floor by estimating a realistic 3D RIS beam response model from on-site measurements. The proposed calibration algorithm first extracts the RIS-reflected channel response from signals received by a calibration agent sampling the angular range of interest, using delay-domain sparse recovery, and then estimates the beam model parameters with a gradient-based estimator. To validate the proposed framework, 3D beam patterns under 66 phase modulations were measured and incorporated into simulations. With an angular sampling step of 1 deg, the calibrated model achieves an average beam response similarity of 88.5% with respect to the ground truth, compared with 43.7% for the ideal model. The probability that the absolute lower bound of the positioning error is below 0.5m increases from 0.52 without calibration to 0.74 after calibration, showing that on-site RIS beam calibration effectively reduces the positioning error floor caused by true beam model mismatch.

To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao test, Wald test, gradient test, and Durbin test. The results show that the detectors based on GLRT, Rao and Wald are structurally consistent with the subspace detectors in zero-means clutter. The analytic expressions for the probability of detection (PD) and probability of false alarm (PFA) of each detector are derived, and two major performance differences in the nonzero-mean clutter scenario are revealed. One is the loss of degree of freedom (DOF), which is reduced by 1 compared with the zero-mean clutter scenario. The second is the loss of signal-to-clutter (SCR) ratio. Simulation and measured data verify the effectiveness of the proposed detectors and demonstrate their practical value in real-world radar systems.

In this letter, we devise two moving sampling schemes for two-dimensional (2D) direction-of-arrival (DOA) estimation using sparse planar arrays. The proposed schemes are applied to two different kinds of sparse planar arrays on a moving platform. By designing the appropriate motion trail, the proposed schemes can joint the uniform rectangular parts of the synthetic difference coarray together to yield a larger uniform rectangular array (URA). Compared with the traditional virtual array construction, our schemes can generate a larger virtual URA, and thus result in a significant increase in the number of uniform degrees of freedom (DOFs). Numerical results demonstrate the advantages of the proposed schemes in the number of uniform DOFs and DOA estimation accuracy.

This paper investigates the optimization of reconfigurable intelligent surfaces (RIS) for near-field user equipment (UE) localization in the presence of channel spatial non-stationarity (SNS) across elements and the involved deliberate model misspecification. Traditional far-field localization techniques struggle to maintain accuracy in near-field scenarios characterized by spherical wavefronts (SWFs) while algorithms relying on a full-fledged near-field model with SNS and SWF necessitate long transmission durations to estimate a large number of parameters. To address these challenges, we propose a novel low-complexity localization algorithm based on a misspecified model that reduces the number of unknown channel parameters with limited impact on accuracy. A misspecified Cram & eacute;r-Rao bound (MCRB) analysis is also performed to evaluate theoretical performance degradation due to the involved model misspecification. Additionally, we propose a codebook design that leverages coarse UE location information to enhance localization accuracy. Numerical simulations validate the effectiveness of the proposed estimation method and highlight the advantages of using the proposed codebook.

This paper studies user localization aided by a Reconfigurable Intelligent Surface (RIS). A feedback link from the Base Station (BS) to the user is adopted to enable dynamic power control of the user pilot transmissions in the uplink. A novel multi-agent algorithm for the joint control of the RIS phase configuration and the user transmit power is presented, which is based on a hybrid approach integrating NeuroEvolution (NE) and supervised learning. The proposed scheme requires only single-bit feedback messages for the uplink power control, supports RIS elements with discrete responses, and is numerically shown to outperform fingerprinting, deep reinforcement learning baselines and backpropagation-based position estimators.

As an emerging wide bandgap semiconductor material, (3-Ga2O3 single crystal has shown great application prospects in power devices, solar-blind detectors and gas sensors. However, its significant anisotropy and low mechanical strength lead to the occurrence of delamination and fragmentation in traditional processing, making it difficult to achieve high-precision complex processing. In this paper, micro-nano processing was carried out using picosecond pulsed laser. The damage threshold of (010) surface (3-Ga2O3 under single picosecond pulsed laser irradiation was determined to be approximately 1.85 J/cm2 by the damage probability extrapolation method. With the increase of pulse number, the damage threshold decreased significantly and then tended to be stable, and the laser damage resistance of the material gradually tended to be stable. By controlling the laser energy density, the spatial frequency of the periodic structure can be regulated, and the selective preparation of high and low spatial frequency surface periodic structures was achieved.

In smart city development, the automatic detection of structures and vehicles within urban or suburban areas via array radar (airborne or vehicle platforms) becomes crucial. However, the inescapable multipath effect adversely affects the radar's capability to detect and track targets. Frequency Diversity Array (FDA)-MIMO radar offers innovative solutions in mitigating multipath due to its frequency flexibility and waveform diversity traits amongst array elements. Hence, utilizing FDA-MIMO radar, this research proposes a multipath discrimination and suppression strategy to augment target detection and suppress false alarms. The primary advancement is the transformation of conventional multipath suppression into a multipath recognition issue, thereby enabling multipath components from single-frame echo data to be separated without prior knowledge. By offsetting the distance steering vectors of different objects to be detected, the accurate spectral information corresponding to the current distance unit can be extracted during spatial spectrum estimation. The direct and multipath components are differentiated depending on whether the transmitting and receiving angles match. Additionally, to mitigate high-order multipath, the echo intensity of multipath components is reduced via joint optimization of array transmit weighting and frequency increment. The numerical results show that the proposed algorithm can identify multipath at different distances in both single-target and multi-target scenarios, which is superior to the general MIMO radar.

For solving the detection challenge of distributed targets in compound-Gaussian clutter under steering vector uncertainty, we propose two efficient detectors based on the generalized likelihood ratio test (GLRT) and Wald test under the assumptions that the clutter texture is deterministic but unknown and the distributed target steering vector is confined to a specified subspace while its coordinates remain undetermined. In the parameter estimation phase, we use maximum likelihood estimation to estimate target amplitude and texture, followed by target coordinate estimation via the projected gradient descent method. In the detector design phase, we adopt a two-step strategy. First, we derive detectors under known clutter covariance matrix (CM). Subsequently, the CM is substituted with its approximate maximum likelihood estimate. Simulation and real-data experiments confirm that the developed detectors exhibit superior detection performance compared to existing methods, with the GLRT-based detector achieving better results than the Wald-based detector. Moreover, both detectors' detection performance improves with increased training data, reduced target distributed dimension, and signal subspace dimension. In addition, the proposed detectors exhibit constant false alarm rate properties for texture and CM structure.

Beamforming plays a crucial role in millimeter wave (mmWave) communication systems to mitigate the severe attenuation inherent to this spectrum. However, the use of large active antenna arrays in conventional architectures often results in high implementation costs and excessive power consumption, limiting their practicality. As an alternative, deploying large arrays at transceivers using passive devices, such as reconfigurable intelligent surfaces (RISs), offers a more cost-effective and energy-efficient solution. In this paper, we investigate a promising base station (BS) architecture that integrates a beyond diagonal RIS (BD-RIS) within the BS to enable passive beamforming. By utilizing Takagi's decomposition and leveraging the effective beamforming vector, the RIS profile can be designed to enable passive beamforming directed toward the target. Through the beamforming analysis, we reveal that BD-RIS provides robust beamforming performance across various system configurations, whereas the traditional diagonal RIS (D-RIS) exhibits instability with increasing RIS size and decreasing BS-RIS separation-two critical factors in optimizing RIS-assisted systems. Comprehensive computer simulation results across various aspects validate the superiority of the proposed BS-integrated BD-RIS over conventional D-RIS architectures, showcasing performance comparable to active analog beamforming antenna arrays.