CyberSec Research

The global navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerging applications such as autonomous driving and smart environmental monitoring. However, these advancements are accompanied by a rise in interference threats, which can significantly compromise the reliability and safety of GNSS. Such interference problems are typically addressed through signal-processing techniques that rely on physics-based mathematical models. Unfortunately, solutions of this nature can often fail to fully capture the complex forms of interference. To address this, artificial intelligence (AI)-inspired solutions are expected to play a key role in future interference management solutions, thanks to their ability to exploit data in addition to physics-based models. This magazine paper discusses the main challenges and tasks required to secure GNSS and present a research vision on how AI can be leveraged towards achieving more robust GNSS-based positioning.

We consider the problem of designing a data-driven nonlinear state estimation (DANSE) method that uses (noisy) nonlinear measurements of a process whose underlying state transition model (STM) is unknown. Such a process is referred to as a model-free process. A recurrent neural network (RNN) provides parameters of a Gaussian prior that characterize the state of the model-free process, using all previous measurements at a given time point. In the case of DANSE, the measurement system was linear, leading to a closed-form solution for the state posterior. However, the presence of a nonlinear measurement system renders a closed-form solution infeasible. Instead, the second-order statistics of the state posterior are computed using the nonlinear measurements observed at the time point. We address the nonlinear measurements using a reparameterization trick-based particle sampling approach, and estimate the second-order statistics of the state posterior. The proposed method is referred to as particle-based DANSE (pDANSE). The RNN of pDANSE uses sequential measurements efficiently and avoids the use of computationally intensive sequential Monte-Carlo (SMC) and/or ancestral sampling. We describe the semi-supervised learning method for pDANSE, which transitions to unsupervised learning in the absence of labeled data. Using a stochastic Lorenz-$63$ system as a benchmark process, we experimentally demonstrate the state estimation performance for four nonlinear measurement systems. We explore cubic nonlinearity and a camera-model nonlinearity where unsupervised learning is used; then we explore half-wave rectification nonlinearity and Cartesian-to-spherical nonlinearity where semi-supervised learning is used. The performance of state estimation is shown to be competitive vis-\`a-vis particle filters that have complete knowledge of the STM of the Lorenz-$63$ system.

According to the Paris Climate Change Agreement, all nations are required to submit reports on their greenhouse gas emissions and absorption every two years by 2024. Consequently, forests play a crucial role in reducing carbon emissions, which is essential for meeting these obligations. Recognizing the significance of forest conservation in the global battle against climate change, Article 5 of the Paris Agreement emphasizes the need for high-quality forest data. This study focuses on enhancing methods for mapping aboveground biomass in tropical dry forests. Tropical dry forests are considered one of the least understood tropical forest environments; therefore, there is a need for accurate approaches to estimate carbon pools. We employ a comparative analysis of AGB estimates, utilizing different discrete and full-waveform laser scanning datasets in conjunction with Ordinary Least Squares and Bayesian approaches SVM. Airborne Laser Scanning, Unmanned Laser Scanning, and Space Laser Scanning were used as independent variables for extracting forest metrics. Variable selection, SVM regression tuning, and cross-validation via a machine-learning approach were applied to account for overfitting and underfitting. The results indicate that six key variables primarily related to tree height: Elev.minimum, Elev.L3, lev.MAD.mode, Elev.mode, Elev.MAD.median, and Elev.skewness, are important for AGB estimation using ALSD and ULSD , while Leaf Area Index, canopy coverage and height, terrain elevation, and full-waveform signal energy emerged as the most vital variables. AGB values estimated from ten permanent tropical dry forest plots in Costa Rica Guanacaste province ranged from 26.02 Mg/ha to 175.43 Mg/ha . The SVM regressions demonstrated a 17.89 error across all laser scanning systems, with SLSF W exhibiting the lowest error 17.07 in estimating total biomass per plot.

In this paper, we propose a unified localization framework (called UNILocPro) that integrates model-based localization and channel charting (CC) for mixed line-of-sight (LoS)/non-line-of-sight (NLoS) scenarios. Specifically, based on LoS/NLoS identification, an adaptive activation between the model-based and CC-based methods is conducted. Aiming for unsupervised learning, information obtained from the model-based method is utilized to train the CC model, where a pairwise distance loss (involving a new dissimilarity metric design), a triplet loss (if timestamps are available), a LoS-based loss, and an optimal transport (OT)-based loss are jointly employed such that the global geometry can be well preserved. To reduce the training complexity of UNILocPro, we propose a low-complexity implementation (called UNILoc), where the CC model is trained with self-generated labels produced by a single pre-training OT transformation, which avoids iterative Sinkhorn updates involved in the OT-based loss computation. Extensive numerical experiments demonstrate that the proposed unified frameworks achieve significantly improved positioning accuracy compared to both model-based and CC-based methods. Notably, UNILocPro with timestamps attains performance on par with fully-supervised fingerprinting despite operating without labelled training data. It is also shown that the low-complexity UNILoc can substantially reduce training complexity with only marginal performance degradation.

Early fault diagnosis is imperative for the proper functioning of rotating machines. It can reduce economic losses in the industry due to unexpected failures. Existing fault analysis methods are either expensive or demand expertise for the installation of the sensors. This article proposes a novel method for the detection of bearing faults and imbalance in induction motors using an antenna as the sensor, which is noninvasive and cost-efficient. Time-varying S11 is measured using an omnidirectional antenna, and it is seen that the spectrogram of S11 shows unique characteristics for different fault conditions. The experimental setup has analytically evaluated the vibration frequencies due to fault and validated the characteristic fault frequency by applying FFT analysis on the captured S11 data. This article has evaluated the average power content of the detected signals at normal and different fault conditions. A deep learning model is used to classify the faults based on the reflection coefficient ( S11). It is found that classification accuracy of 98.2% is achieved using both magnitude and phase of S11, 96% using the magnitude of S11 and 92.1% using the phase of S11. The classification accuracy for different operating frequencies, antenna location, and time windows are also investigated.

This article investigates how the creeping wave propagation around the human thigh could be used to monitor the leg movements. The propagation path around the human thigh gives information regarding leg motions that can be used for the classification of activities. The variation of the transmission coefficient is measured between two on-body polyethylene terephthalate (PET) flexible antennas for six different leg-based activities that exhibit unique time-varying signatures. A discrete wavelet transform (DWT) along with different classifiers, such as support vector machine (SVM), decision trees, naive Bayes, and K-nearest neighbors (KNN), is applied for feature extraction and classification to evaluate the efficiency for classifying different activity signals. Additional algorithms, such as dynamic time warping (DTW) and deep convolutional neural network (DCNN), have also been implemented, and in each case, SVM with DWT outperforms the others. Simulation to evaluate a specific absorption rate (SAR) is carried out as the antenna is positioned on the human thigh leaving no gap. The results show that the SAR is within the threshold as per the Federal Communications Commission (FCC) standard.

Low-earth-orbit (LEO) satellite communication systems that use millimeter-wave frequencies rely on large antenna arrays with hybrid analog-digital architectures for rapid beam steering. LEO satellites are only visible from the ground for short periods of times (a few tens of minutes) due to their high orbital speeds. This paper presents a variational message passing algorithm for joint localization and beam tracking of a LEO satellite from a ground station equipped with a hybrid transceiver architecture. The algorithm relies on estimating the parameters of the orbit, which is modelled as circular. Angles are then obtained from the orbit in a straightforward manner. Simulation results show that the proposed method is highly resilient to missed detections, enables reliable satellite tracking even near the horizon, and effectively alleviates the ambiguities inherent in hybrid architectures.

As people nowadays increasingly rely on artificial intelligence (AI) to curate information and make decisions, assigning the appropriate amount of trust in automated intelligent systems has become ever more important. However, current measurements of trust in automation still largely rely on self-reports that are subjective and disruptive to the user. Here, we take music recommendation as a model to investigate the neural and cognitive processes underlying trust in automation. We observed that system accuracy was directly related to users' trust and modulated the influence of recommendation cues on music preference. Modelling users' reward encoding process with a reinforcement learning model further revealed that system accuracy, expected reward, and prediction error were related to oscillatory neural activity recorded via EEG and changes in pupil diameter. Our results provide a neurally grounded account of calibrating trust in automation and highlight the promises of a multimodal approach towards developing trustable AI systems.

To further suppress the inherent self-interference (SI) in co-frequency and co-time full-duplex (CCFD) systems, we propose integrating a stacked intelligent metasurface (SIM) into the RF front-end to enhance signal processing in the wave domain. Furthermore, an end-to-end (E2E) learning-based signal processing method is adopted to control the metasurface. Specifically, the real metasurface is abstracted as hidden layers of a network, thereby constructing an electromagnetic neural network (EMNN) to enable driving control of the real communication system. Traditional communication tasks, such as channel coding, modulation, precoding, combining, demodulation, and channel decoding, are synchronously carried out during the electromagnetic (EM) forward propagation through the metasurface. Simulation results show that, benefiting from the additional wave-domain processing capability of the SIM, the SIM-assisted CCFD system achieves significantly reduced bit error rate (BER) compared with conventional CCFD systems. Our study fully demonstrates the potential applications of EMNN and SIM-assisted E2E CCFD systems in next-generation transceiver design.

In next-generation wireless systems, providing location-based mobile computing services for energy-neutral devices has become a crucial objective for the provision of sustainable Internet of Things (IoT). Visible light positioning (VLP) has gained great research attention as a complementary method to radio frequency (RF) solutions since it can leverage ubiquitous lighting infrastructure. However, conventional VLP receivers often rely on photodetectors or cameras that are power-hungry, complex, and expensive. To address this challenge, we propose a hybrid indoor asset tracking system that integrates visible light communication (VLC) and backscatter communication (BC) within a simultaneous lightwave information and power transfer (SLIPT) framework. We design a low-complexity and energy-neutral IoT node, namely backscatter device (BD) which harvests energy from light-emitting diode (LED) access points, and then modulates and reflects ambient RF carriers to indicate its location within particular VLC cells. We present a multi-cell VLC deployment with frequency division multiplexing (FDM) method that mitigates interference among LED access points by assigning them distinct frequency pairs based on a four-color map scheduling principle. We develop a lightweight particle filter (PF) tracking algorithm at an edge RF reader, where the fusion of proximity reports and the received backscatter signal strength are employed to track the BD. Experimental results show that this approach achieves the positioning error of 0.318 m at 50th percentile and 0.634 m at 90th percentile, while avoiding the use of complex photodetectors and active RF synthesizing components at the energy-neutral IoT node. By demonstrating robust performance in multiple indoor trajectories, the proposed solution enables scalable, cost-effective, and energy-neutral indoor tracking for pervasive and edge-assisted IoT applications.

It is known that the minimum-mean-squared-error (MMSE) denoiser under Gaussian noise can be written as a proximal operator, which suffices for asymptotic convergence of plug-and-play (PnP) methods but does not reveal the structure of the induced regularizer or give convergence rates. We show that the MMSE denoiser corresponds to a regularizer that can be written explicitly as an upper Moreau envelope of the negative log-marginal density, which in turn implies that the regularizer is 1-weakly convex. Using this property, we derive (to the best of our knowledge) the first sublinear convergence guarantee for PnP proximal gradient descent with an MMSE denoiser. We validate the theory with a one-dimensional synthetic study that recovers the implicit regularizer. We also validate the theory with imaging experiments (deblurring and computed tomography), which exhibit the predicted sublinear behavior.

Integrated sensing and communication (ISAC) is a key feature of next-generation wireless networks, enabling a wide range of emerging applications such as vehicle-to-everything (V2X) and unmanned aerial vehicles (UAVs), which operate in high-mobility scenarios. Notably, the wireless channels within these applications typically exhibit severe delay and Doppler spreads. The latter causes serious communication performance degradation in the Orthogonal Frequency-Division Multiplexing (OFDM) waveform that is widely adopted in current wireless networks. To address this challenge, the recently proposed Doppler-resilient affine frequency division multiplexing (AFDM) waveform, which uses flexible chirp signals as subcarriers, shows great potential for achieving adaptive ISAC in high-mobility scenarios. This article provides a comprehensive overview of AFDM-ISAC. We begin by presenting the fundamentals of AFDM-ISAC, highlighting its inherent frequency-modulated continuous-wave (FMCW)-like characteristics. Then, we explore its ISAC performance limits by analyzing its diversity order, ambiguity function (AF), and Cramer-Rao Bound (CRB). Finally, we present several effective sensing algorithms and opportunities for AFDM-ISAC, with the aim of sparking new ideas in this emerging field.

This paper proposes a novel beamforming framework in the reproducing kernel domain, derived from a unified interpretation of directional response as spatial differentiation of the sound field. By representing directional response using polynomial differential operators, the proposed method enables the formulation of arbitrary beam patterns including non-axisymmetric. The derivation of the reproducing kernel associated with the interior fields is mathematically supported by Hobson's theorem, which allows concise analytical expressions. Furthermore, the proposed framework generalizes conventional spherical harmonic domain beamformers by reinterpreting them as spatial differential operators, thereby clarifying their theoretical structure and extensibility. Three numerical simulations conducted in two-dimensional space confirm the validity of the method.

In this paper, we address the problem of reconstructing multiband signals from modulo-folded, pointwise samples within the Unlimited Sensing Framework (USF). Focusing on a low-complexity, single-channel acquisition setup, we establish recovery guarantees demonstrating that sub-Nyquist sampling is achievable under the USF paradigm. In doing so, we also tighten the previous sampling theorem for bandpass signals. Our recovery algorithm demonstrates up to a 13x dynamic range improvement in hardware experiments with up to 6 spectral bands. These results enable practical high-dynamic-range multiband acquisition in scenarios previously limited by dynamic range and excessive oversampling.

Massive message transmissions, unpredictable aperiodic messages, and high-speed moving vehicles contribute to the complex wireless environment, resulting in inefficient resource collisions in Vehicle to Everything (V2X). In order to achieve better medium access control (MAC) layer performance, 3GPP introduced several new features in NR-V2X. One of the most important is the re-evaluation mechanism. It allows the vehicle to continuously sense resources before message transmission to avoid resource collisions. So far, only a few articles have studied the re-evaluation mechanism of NR-V2X, and they mainly focus on network simulator that do not consider variable traffic, which makes analysis and comparison difficult. In this paper, an analytical model of NR-V2X Mode 2 is established, and a message generator is constructed by using discrete time Markov chain (DTMC) to simulate the traffic pattern recommended by 3GPP advanced V2X services. Our study shows that the re-evaluation mechanism improves the reliability of NR-V2X transmission, but there are still local improvements needed to reduce latency.

Aggregating intracranial recordings across subjects is challenging since electrode count, placement, and covered regions vary widely. Spatial normalization methods like MNI coordinates offer a shared anatomical reference, but often fail to capture true functional similarity, particularly when localization is imprecise; even at matched anatomical coordinates, the targeted brain region and underlying neural dynamics can differ substantially between individuals. We propose a scalable representation-learning framework that (i) learns a subject-agnostic functional identity for each electrode from multi-region local field potentials using a Siamese encoder with contrastive objectives, inducing an embedding geometry that is locality-sensitive to region-specific neural signatures, and (ii) tokenizes these embeddings for a transformer that models inter-regional relationships with a variable number of channels. We evaluate this framework on a 20-subject dataset spanning basal ganglia-thalamic regions collected during flexible rest/movement recording sessions with heterogeneous electrode layouts. The learned functional space supports accurate within-subject discrimination and forms clear, region-consistent clusters; it transfers zero-shot to unseen channels. The transformer, operating on functional tokens without subject-specific heads or supervision, captures cross-region dependencies and enables reconstruction of masked channels, providing a subject-agnostic backbone for downstream decoding. Together, these results indicate a path toward large-scale, cross-subject aggregation and pretraining for intracranial neural data where strict task structure and uniform sensor placement are unavailable.

Protecting radio astronomy observatories from unintended interference is critical as wireless transmissions increases near protected bands. While database-driven coordination frameworks and radio quiet zones exist, they cannot rapidly identify or suppress specific interfering transmitters, especially at low signal-to-noise ratio (SNR) levels. This paper presents the first over-the-air field demonstration of Pseudonymetry at the Owens Valley Radio Observatory (OVRO), illustrating cooperative spectrum sharing between heterogeneous wireless systems. In our experiment, a narrow-band secondary transmitter embeds a pseudonym watermark into its signal, while the wide-band radio telescope passively extracts the watermark from spectrogram data. Results show that interference can be reliably detected and the interfering device uniquely identified even at low SNR where conventional demodulation is infeasible. These findings validate that passive scientific receivers can participate in a lightweight feedback loop to trigger shutdown of harmful transmissions, demonstrating the potential of Pseudonymetry as a complementary enforcement tool for protecting radio astronomy environments.

Cell-free massive multiple-input multiple-output (MIMO) is a key technology for next-generation wireless systems. The integration of cell-free massive MIMO within the open radio access network (O-RAN) architecture addresses the growing need for decentralized, scalable, and high-capacity networks that can support different use cases. Precoding is a crucial step in the operation of cell-free massive MIMO, where O-RUs steer their beams towards the intended users while mitigating interference to other users. Current precoding schemes for cell-free massive MIMO are either fully centralized or fully distributed. Centralized schemes are not scalable, whereas distributed schemes may lead to a high inter-O-RU interference. In this paper, we propose a distributed and scalable precoding framework for cell-free massive MIMO that uses limited information exchange among precoding agents to mitigate interference. We formulate an optimization problem for precoding that maximizes the aggregate throughput while guaranteeing the minimum data rate requirements of users. The formulated problem is nonconvex. We propose a multi-timescale framework that combines multi-agent deep reinforcement learning (DRL) with expert insights from an iterative algorithm to determine the precoding matrices efficiently. We conduct simulations and compare the proposed framework with the centralized precoding and distributed precoding methods for different numbers of O-RUs, users, and transmit antennas. The results show that the proposed framework achieves a higher aggregate throughput than the distributed regularized zero-forcing (D-RZF) scheme and the weighted minimum mean square error (WMMSE) algorithm. When compared with the centralized regularized zero-forcing (C-RZF) scheme, the proposed framework achieves similar aggregate throughput performance but with a lower signaling overhead.