Peng Chen

Electromagnetic functional surfaces capable of multimode electromagnetic (EM) reconfiguration and reliable operation under extreme thermal environments are highly desirable for advanced aerospace and defense systems. However, simultaneously achieving flexible functional switching, ultra-wideband stealth capability, and thermal robustness remains challenging. Here, a metasurface-aerogel-composite reconfigurable electromagnetic functional surface (REFS) is proposed. The proposed architecture consists of three key components: an absorption-transmission resistive metasurface (AT-RMS) for low-frequency wideband absorption, a transmission-scattering diffusive metasurface (TS-DMS) for high-frequency wideband diffuse scattering, and an aerogel composite layer for thermal protection and impedance optimization. Through the synergistic integration of these functional components, coordinated absorption loss and scattering manipulation are achieved, enabling ultra-wideband radar cross-section (RCS) reduction outside the operating band while maintaining low-loss in-band transmission performance. Furthermore, PIN diode-based reconfigurable elements enable dynamic in-band switching among transmission (T), reflection (R), and absorption (A) states, while the polarization-independent biasing strategy further extends the reconfigurability to nine distinct operating modes. The employed aerogel composite not only provides effective thermal protection with an ultralow thermal conductivity of 0.0176 W/(m · K) but also improves impedance matching through its low permittivity, thereby enhancing the overall EM performance. A 10 × 10 array prototype is fabricated and experimentally characterized to verify the multifunctional and thermally robust performance of the proposed REFS. The fabricated prototype achieves stable T/R/A reconfigurable operation within 5.74–6.23 GHz, a 7-dB ultra-wideband RCS reduction from 2.18 to 17.86 GHz in the transmission state, and maintains stable EM performance after exposure to 600 K for 1 h. These results demonstrate a promising strategy for integrating thermal protection, multimode EM reconfigurability, and ultra-wideband stealth capability into advanced aerospace electromagnetic skins and multifunctional stealth radome systems.

Partially connected hybrid analog-digital beamforming architectures substantially reduce the hardware cost and power dissipation of massive antenna arrays by routing multiple antenna elements to a single radio-frequency (RF) chain. In contested or dense electromagnetic environments, however, strong directional interference entering low-resolution analog-to-digital converters (ADCs) increases signal-dependent quantization distortion and can exceed the converter range if gain control is inadequate. To overcome this fundamental bottleneck, this paper develops a quantization-aware hybrid anti-interference receiver for partially connected architectures constrained by finite-resolution phase shifters and low-bit ADCs. The proposed receiver operates in three coordinated stages. In the spatial sensing stage, the receiver sequentially applies multiple pseudo-random analog combining configurations to collect compressed spatial observations, and reconstructs the angular power spectrum of unknown interference sources via an additive quantization noise model (AQNM)-corrected nonnegative sparse covariance fitting problem solved by the fast iterative shrinkage-thresholding algorithm (FISTA). In the analog beamforming stage, using the reconstructed interference-plus-noise covariance, subarray analog combiners are optimized directly on the complex constant-modulus manifold via Riemannian gradient descent and mapped to discrete phase states, forming analog spatial nulls to reduce interference before quantization. In the digital combining stage, a diagonally loaded robust minimum variance distortionless response (R-MVDR) combiner is formulated directly on the quantized RF domain to suppress residual interference and tolerate covariance estimation errors. Comprehensive simulations demonstrate that the proposed receiver improves output signal-to-interference-plus-noise ratio (SINR) relative to the evaluated hybrid baselines, maintaining substantial performance advantages over conventional hybrid beamforming baselines across diverse interference powers, ADC bit depths (3–8 bits), coarse phase quantization (3–8 bits), reduced RF chain counts, and limited snapshot budgets.

High-mobility dual-functional radar–communication systems require a common waveform that combines delay–Doppler information organization, sensing resolution, and power-efficient transmission. We present a cyclically closed constant-envelope orthogonal time frequency space–continuous phase modulation–linear frequency modulation (OTFS–CPM–LFM) waveform and matched transceiver architecture. Hermitian delay–Doppler mapping and direct-current (DC) row nulling create a real, zero-sum drive with a reversible frame-level phase representation. The communication receiver combines a Tikhonov-regularized waveform inverse with reference-aided unwrapping and tail-biting phase regression, while the radar receiver reconstructs the data-dependent current-frame reference. Numerical results verify the structural waveform properties and characterize communication, radar, and computational tradeoffs. They also quantify degradation under controlled complex-gain channel-state-information mismatch and show that phase regression is less reliable at a low signal-to-noise ratio (SNR). The constant-envelope claim applies only to ideal discrete complex-baseband samples and does not include pulse shaping or radio-frequency hardware. The framework therefore provides a self-consistent waveform interface while exposing tradeoffs among payload, recovery reliability, sensing sidelobes, and implementation cost.

This paper investigates a near-field millimeter-wave (mmWave) integrated sensing and communication (ISAC) system under unknown interference. A base station equipped with a partially connected dynamic subarray hybrid architecture serves a legitimate user while performing target-oriented transmit beampattern shaping. Unlike existing works that assume perfect interference knowledge, we characterize the unknown interference channels via a robust spatial covariance uncertainty model. To exploit spatial degrees of freedom for interference suppression, the user employs a fully connected hybrid receiver. We formulate a robust transmit power minimization problem subject to worst-case communication signal-to-interference-plus-noise ratio (SINR) and sensing beampattern constraints, alongside constant-modulus and dynamic subarray hardware constraints. To solve this highly non-convex mixed discrete–continuous problem, we propose a two-layer alternating optimization framework. The inner layer optimizes the continuous and phase-quantized beamformers using successive convex approximation, while the outer layer refines the binary subarray connections via a penalty-augmented local discrete search. Extensive simulations demonstrate that explicitly modeling worst-case uncertainties ensures reliable ISAC performance in adversarial environments, and the dynamic subarray architecture systematically outperforms conventional fixed topologies in power efficiency. Additional robustness and sensitivity analyses show that these gains are most pronounced when sufficient spatial degrees of freedom remain, whereas excessive antenna failures, unmodeled strong multipath, or covariance drift outside the uncertainty envelope can erode the communication and sensing margins.

With the growing deployment of millimeter-wave radars in vehicle-to-everything (V2X) networks for cooperative perception, exchanging radar sensing data among participants can improve overall situational awareness. Lossless sharing of full-resolution radar data places a heavy burden on communication resources, whereas lossy compression can exploit the sparsity structure of radar point clouds to reduce bitrate. In this paper, we propose an end-to-end kernel-point radar codec (KPRC) for 4-dimensional (4D) millimeter-wave radar point cloud compression and reconstruction. To match the characteristics of radar point clouds, KPRC uses a normalized radar kernel domain in which radial velocity participates in neighbor search and kernel correlation, while SNR is reconstructed as an attribute. Supervised training is performed on point clouds extracted from millimeter-wave radar measurements collected and processed in a roadside traffic scenario. The encoder maps each frame to a quantized latent vector for transmission, and the decoder reconstructs the radar point cloud from the received latent. Compared with the geometry-based point cloud compression (G-PCC) method, KPRC achieves lower rates at comparable reconstruction accuracy in our experiments.

Uplink emergency communications for LEO satellites are vulnerable to severe co-channel interference from dense terrestrial networks. This paper proposes a database-aided robust adaptive beamforming framework that treats database angles as coarse priors and compensates for spatial mismatches. The method includes three stages: (1) local spatial refinement via constrained Capon search to accurately locate interferers while protecting the desired signal; (2) truncated adaptive amplitude (TAA) estimation to recover true interference powers and suppress noise artifacts; and (3) reconstruction of the interference-plus-noise covariance matrix (INCM) for MVDR beamforming. By avoiding global angular scanning and improving spatial statistics estimation, the approach achieves near-optimal SINR under limited snapshots and strong interference. Simulations show consistent performance gains over DL-MVDR, ESB, IAA, and existing database-assisted methods across varying SNR, INR, and mismatch conditions, demonstrating strong robustness and practical applicability.

Signals of opportunity (SoO) enable emission-free passive sensing, but low Earth orbit (LEO) satellite illumination with unmanned aerial vehicle (UAV) array receivers exhibits rapid geometry variation. As a result, the received phase evolves in a space–time coupled manner, and the array snapshots become nonstationary even within one coherent processing interval (CPI), degrading conventional stationary-snapshot direction-of-arrival (DOA) estimators. This paper proposes a decomposition-based sparse reconstruction with successive interference cancellation (D-SR-SIC) framework for dynamic DOA estimation in LEO SoO UAV passive sensing. The proposed estimator leverages a sparse-reconstruction signal model and is implemented via a computationally efficient decomposition-based search-and-cancel procedure. A short-CPI parameterized space–time phase model captures the common motion-induced phase history and the time-varying steering drift; the coupled multi-parameter estimation is decomposed into two low-dimensional correlation searches followed by least-squares amplitude estimation and multi-target peeling. Optional local refinement and multi-beam pre-screening improve robustness to off-grid mismatch, near–far interference, and wide field-of-view operation. Simulations show that the proposed method achieves about 0.11° DOA root-mean-square error (RMSE) at −20 dB signal-to-noise ratio (SNR) in a representative highly dynamic setting.

In reconfigurable intelligent surface (RIS)-assisted communication systems, the amplitudes and phases of reflected electromagnetic waves are modified to enhance signal power and provide additional communication paths, particularly in scenarios lacking a line-of-sight (LOS) propagation path. However, conventional RIS-aided communication systems reflect all incident signals indiscriminately, without distinguishing between desired signals and interference. In this paper, we propose a novel RIS-assisted communication system capable of selectively reflecting only the desired signal by performing interference suppression directly at the RIS. By exploiting the degree of freedom (DOF) in spatial domain of RIS, the design of RIS coefficients is formulated as a minimum variance distortionless response (MVDR) problem, where the optimal RIS coefficients are obtained by estimating the interference subspace and the desired signal. Then, we propose a two-step iterative approach based on atomic norm minimization (ANM) to estimate the subspace and desired signal simultaneously. The resulting ANM problem is solved with alternative optimization by decomposing it into two subproblem. Each subproblem is formulated as semidefinite programming (SDP) problem and efficiently solved using the alternating direction method of multipliers (ADMM). Theoretical analysis is conducted to verify the estimations are consistent and bounded. Simulation results validate its effectiveness and superiority over existing methods.

Graph total variation (GTV) has been widely adopted for hyperspectral image (HSI) denoising. However, the fixed graph weights make it difficult to preserve or enhance important details. Moreover, such methods often focus on preserving spatial textures while neglecting the extraction of spectral texture information, failing to achieve a desirable balance between noise removal and spatial-spectral texture preservation. To address these shortcomings, this letter proposes a novel regularization, namely Spatial-spectral Texture Preserved GTV (SSTPGTV), which features two notable highlights. Firstly, we encode the spatial correlation between pixels via GTV in a band-by-band manner, and the graph weights are adaptively tuned according to the updated HSI data, ensuring that the refined details are preserved well. Secondly, we devise a novel weight learning strategy based on local line binary pattern (LLBP) to capture texture features along the spectral dimension. In addition, due to the fewer bands of the reduced image, SSTPGTV not only fully characterizes spectral correlation, but also maintains low computational complexity. Experimental results demonstrate that our method outperforms most mainstream denoising methods in terms of both performance and computational complexity.