Hongbin Li

Radio interferometric imaging is sensitive to front-end dynamic-range limitations as it relies on antenna visibilities formed from digitized samples. Under strong satellite interference, conventional finite-dynamic-range analog-to-digital converters (ADCs) used for digitization can saturate, introducing nonlinear distortion in the samples. The resulting bias in visibilities undermines interference cancellation, leading to severe imaging artefacts. To address these limitations, we develop a modulo-sampling-enabled interferometric imaging framework. Unlike conventional ADC, modulo sampling is a new data acquisition scheme that folds out-of-range signals into the dynamic range. Within this framework, imaging is carried out either through a reconstruction-based approach, which first reconstructs antenna-level observations from the modulo samples and then applies an ambiguity-correction step, or through a direct-imaging alternative that bypasses reconstruction and operates on differenced modulo samples. Interference is suppressed via visibility-domain eigen-subspace projection, and a temporal smoothing step compensates for projection-induced bias by exploiting the rapid variability of satellite interference. Simulations on synthetic and real Very Large Array data demonstrate that, in contrast to conventional sampling, which is severely affected by interference-induced saturation, the proposed modulo-enabled framework effectively avoids saturation-related imaging artefacts and enables reliable recovery of weak astronomical sources approaching the ideal imaging benchmark.

We consider the problem of recovering a spectrally sparse signal from its partially observed entries. Traditional approaches leverage the low-rank property of such signals in the Hankel lifting domain. In this domain, we further incorporate subspace knowledge, initially estimated from test data and updated during signal recovery, to enhance performance. To quantify the impact of subspace knowledge on this enhancement, we propose a metric that integrates QR decompositions with the subspace direction number to assess its accuracy, as reflected in the recovery guarantee. The established guarantee demonstrates the local optimality of the proposed nonconvex method for signal recovery. Based on the local optimality, the proposed method achieves exact recovery with high probability, provided that the initial subspace estimate is sufficiently accurate to meet certain conditions. Compared to prior methods without subspace knowledge and those that exploit such knowledge, our method reduces the sample complexity by three and two logarithmic factors, respectively. Furthermore, we employ lowrank factorization techniques to incorporate prior information of the Hankel matrix rank, thereby enhancing the recovery precision and computational efficiency of the proposed method. The proposed method is efficiently optimized in an alternating minimization framework. Experimental results demonstrate the superior recovery precision and reduced sample complexity of the proposed method compared to existing methods, as well as its enhanced performance through low-rank factorization.

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.

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.

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 non-cooperative illuminators of op portunity (IOs). However, the joint processing of active and passive observations, coupled with the unknown signals from non-cooperative IOs, leads to a 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. Wealso derive the Cram´ er–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.

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.

This letter proposes a tensor-based framework for estimating the timing offset (TO) and carrier frequency offset (CFO) between base stations (BSs) in a networked integrated sensing and communication (ISAC) system. Unlike a recently introduced super-resolution offset estimation by matrix pencil (SOE-MP) method that compresses the received signal matrix in BS-BS links into a vector, the proposed framework employs canonical polyadic decomposition (CPD) to preserve and exploit the spatial-time-frequency structure of the data. Through CPD, our framework separates multipath channel components, enabling TO estimation that is robust against multipath delay interference, a major limitation of the SOE-MP method. The proposed CPD framework operates in a joint manner, where the CFO-related factor matrix is shared between two CPDs based on a reciprocity in BS-BS links. The framework also incorporates a Vandermonde structural constraint on the TO- and CFO-related factor matrices. These structured factors can be efficiently optimized via a correlation-based scheme. The Cramér-Rao bound for offset estimation and the uniqueness conditions for the Vandermonde structured CPDs are provided to characterize the theoretical performance limit. Simulation results demonstrate the superior estimation performance of the proposed tensor-based method compared to the conventional approach.

Deceptive jamming presents significant challenges to radar systems, especially Synthetic Aperture Radar (SAR), by creating false targets to disrupt detection and imaging. Modern deceptive jammers increasingly adopt low-resolution quantization, particularly 1-bit quantization, to simplify hardware implementation and reduce complexity while still generating convincing false targets. This operation introduces odd harmonics and spectral distortions into the intercepted signals. This paper proposes two de tection schemes to mitigate deceptive jamming in radar systems: the Temporal Variations Detection (TVD) scheme for single-scatterer scenarios and the Time-Frequency Domain Detection (TFD) scheme for multi-scatterer scenarios. Both schemes leverage the unique spectral and time-frequency characteristics introduced by quantization, a feature often overlooked in prior research. For single scatterer scenarios, the TVD scheme utilizes temporal variations induced by quantization to distinguish between true echoes and a combination of true and deceptive echoes. Theoretical analyses yield closed-form expressions for key performance metrics, including the Probability of Detection (PD) and Probability of False Alarm (PFA), under both accurate and imperfect scattering coefficient estimations. For multi-scatterer scenarios, the TFD scheme combines time frequency domain analysis with feature enhancement techniques to accurately identify false targets in SAR imagery. Simulation results show that the TVD scheme achieves detection results consistent with theoretical predictions, while the TFD scheme not only reaches a block-level PD of 92.37% for stationary jammers and 100% for moving jammers but also accurately identifies decoy targets in real-scene experiments. These results demonstrate the robustness and effectiveness of the proposed methods in addressing deceptive jamming challenges in radar systems.

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.