Integrated sensing and communication (ISAC) is a promising paradigm for next-generation wireless networks, offering enhanced resource utilization and improved performance across diverse applications. However, the inherent openness of wireless environments and the integration of sensing and communication functionalities introduce unique security and privacy challenges. This paper presents a comprehensive survey of the existing literature on security and privacy preservation in ISAC-enabled wireless networks. We first analyze and synthesize current approaches, categorize security solutions based on physical and network layer protections, and then address privacy concerns related to terminal end and ISAC data. Subsequently, we highlight key open issues, including the need for scalable and adaptable security frameworks, trustworthy collaborative ISAC techniques, and standardized regulations for ISAC security and privacy. By providing a structured overview of the field, this survey charts a roadmap for future research, aiming at realizing robust, secure, and privacy-conscious ISAC systems.

In this paper, the problem of distributed state estimation of human-driven vehicles (HDVs) by connected autonomous vehicles (CAVs) is investigated in mixed traffic transportation systems. Toward this, a distributed observable state-space model is derived, which paves the way for estimation and observability analysis of HDVs in mixed traffic scenarios. In this direction, first, we obtain the condition on the network topology to satisfy the distributed observability, i.e., the condition such that each HDV state is observable to every CAV via information-exchange over the network. It is shown that strong connectivity of the network, along with the proper design of the observer gain, is sufficient for this. A distributed observer is then designed by locally sharing estimates/observations of each CAV with its neighborhood. Second, in case there exist faulty sensors or unreliable observation data, we derive the condition for redundant distributed observability as a q-node/link-connected network design. This redundancy is achieved by extra information-sharing over the network and implies that a certain number of faulty sensors and unreliable links can be isolated/removed without losing the observability. Simulation results are provided to illustrate the effectiveness of the proposed approach.

Compressed sensing magnetic resonance imaging (CSMRI), a typically accelerated data acquisition technology in MRI system, aims to recovery high-quality MR images from fewer k-space data. Recent deep unfolded methods have achieved remarkable performance in terms of reconstruction quality, but there is still room for reconstruction performance improvement due to the limitation of representation ability of the prior network. To improve the representation ability of the prior network as well as the reconstruction performance, we propose a novel Transformer–Mamba-based dual frame network named DualTMNet and serving it as a prior network for constructing an unfolded CSMRI algorithm. Besides the synthesis and analysis frames, the adaptive thresholds in dual frame network are also important for high representation ability of the overall network, we elaborate a dynamic threshold generation module to learn the threshold vector from input instances adaptively. Therein, a noisy intensity generation module (NGM) and Transformer–Mamba cross-fusion module (TMCF) are meticulously elaborated in the dynamic threshold generation module. TMCF introduces a Mamba module and a Transformer module to exploit the global contexts and complementary non-local information, respectively. By doing so, the proposed network can effectively extract important features as well as generate the faithful thresholds for image filtering. Via an end-to-end supervised learning strategy, the dynamic threshold generation module and the dual frame network are jointly trained from the labels and their counterparts. Serving the proposed DualTMNet as a prior network, we illustrate its performance on the task of CSMRI. Extensive experiments demonstrate the proposed method can achieve higher-quality reconstructions over the benchmark competitors in terms of both qualitative and quantitative quality.

Future wireless communications systems are expected to operate at bands above 100 GHz. The high energy consumption of analog-to-digital converters, due to their high resolution, represents a bottleneck for future wireless communications systems that require low energy consumption and low complexity devices at the receiver. In this regard, we devise a novel precoding method based on quality of service constraints for a multiuser multiple-input multiple-output downlink system with 1-bit quantization and oversampling. For this scenario, the time-instance zero-crossing modulation, which conveys the information into the zero-crossings, is considered. Unlike prior studies, the constraint is given in terms of the symbol error probability related to the minimum distance to the decision threshold. Numerical results illustrate the performance of the proposed precoding method evaluated under different parameters and scenarios.

The focus of this paper is to improve simulation-based inference through improved denoising of experimental data. Statistical inference of parameters governing a complex physical process from observed data is an important task for several scientific domains. In a likelihood-free inference setup, the inference engine used to make inference from the experimental data can be trained using simulated data. In many scenarios, experimental data is corrupted by noise during the data acquisition process, which is either unaccounted for in the simulator or whose strength might not match with the one set in the simulator. This deteriorates the accuracy of the inference. While advances in denoising can be leveraged to address this challenge, many denoising techniques ignore the fact that experimental data in many scientific domains tends to be irregular/graph-structured. This paper addresses these two challenges by developing a kernel-based learnable graph (KBLG) denoiser, which can be used to denoise experimental graph-structured data. In order to exhibit the efficacy of the developed denoiser in simulation-driven inference problems, this paper considers the problem of inference of the position of an interaction in an astroparticle detector. The available simulated data in this case is the snapshots of the luminous responses of the photomultiplier tube sensors used within the dark matter detection experiment. In experimental situations, these measurements are corrupted by noise generated by secondary optical and electronic processes. The proposed KBLG denoiser is used to denoise the multiple snapshots of the experimental measurements, which are then used for position reconstruction using a multilayer perceptron trained using noiseless simulated data. Numerical results exhibit that the proposed KBLG denoiser outperforms a graph-agnostic denoiser in terms of MLP-based position reconstruction performances for different levels of noise (noise variance).

The automatic classification of electrocardiograms (ECGs) plays a crucial role in the early diagnosis of cardiovascular diseases. In recent research, deep neural network (DNN)-based methods have garnered significant attention due to their exceptional feature extraction capabilities. However, these methods face challenges in dealing with the complex inter- and intra-class dependencies inherent in different arrhythmias. We propose a classification model for electrocardiograms based on the Graph Convolutional Neural Network (EGCNet) to address this. In our EGCNet framework, we first employ the row and column 1-dimensional residual convolution to learn intra-class dependency. Then, we construct a hierarchical graph structure to model the inter-class dependency. In such a graph, each node represents one type of disease, and the edges describe the relationship between diseases. To reduce the complexity of inter-class dependency, we build a hierarchical graph structure by treating similar nodes as the same type. Thus, we can learn the discriminant dependency in the higher-level graph space and feed this high-level knowledge back to the lower-level graph space. Finally, we evaluated our method using publicly available datasets from the PhysioNet/Computing Challenge 2020 ECG data. For instance, our model achieved robust performance in the 24-label experiment with a precision of 0.753 and an F1 score of 0.722.

The dual-functional radar-communication (DFRC) integrated system presents an ideal solution to address the challenge of spectrum resource congestion in future networks. This paper explores an adaptive power allocation technique based on beamforming to enhance the word error probability (WEP) performance of the DFRC system. Initially, a joint optimization model is developed to minimize the WEP while adhering to constraints on radar signal-to-interference-noise ratio (SINR), peak-to-average-power ratio, sidelobe level, and total transmit power. This model incorporates dual-function transmit beam, radar, and communication receive beam patterns. Subsequently, the proposed subproblem convex relaxation alternating update (SCRAU) algorithm is introduced to achieve a locally optimal solution for multi-carrier power allocation. This algorithm decomposes the original non-convex optimization problem into three sub-problems with lower complexity and iteratively optimizes them. Simulation experiments validate that the SCRAU algorithm can simultaneously fulfill radar and communication functions. The SCRAU algorithm demonstrates superior WEP performance compared to current advanced algorithms.

We address the issue of detecting hidden periodicity when the signal exhibits periodic correlation, but is additionally affected by non-Gaussian noise with unknown characteristics. This scenario is common in various applications. The conventional approach for identifying periodically correlated (PC) behavior involves the frequency domain-based analysis. In our investigation, we also employ such an approach; however, we use a robust version of the discrete Fourier transform incorporating the Huber function-based M-estimation, unlike the classical algorithm. Building upon this approach, we propose robust coherent and incoherent statistics originally designed to identify hidden periodicity in pure PC models. The novelty of this paper lies in introducing robust coherent and incoherent statistics through the application of the robust discrete Fourier transform in classical algorithms and proposing a new technique for period estimation based on the proposed methodology. We explore two types of PC models and two types of additive noise, resulting in PC signals disturbed by non-Gaussian additive noise. Detecting hidden periodicity in such cases proves to be significantly more challenging than in classical scenarios. Through Monte Carlo simulations, we demonstrate the effectiveness of the proposed robust approaches and their superiority over classical. To further substantiate our findings, we analyze three datasets in which hidden periodicity had previously been confirmed in the literature. Among them, two datasets correspond to the condition monitoring area, being a main motivation of our research.

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE).

Ionospheric scintillation causes major impairments to Global Navigation Satellite System (GNSS) in low-latitude regions. In severe scenarios, this event can lead to complete loss of lock, thus making GNSS measurements unusable for navigation. In this paper, we derive a cooperative localization algorithm where a set of partially connected aircraft exchange messages with neighboring nodes on the network to improve their own position estimates. We consider the scintillation events as abrupt changes in the measurement variance, which are modeled by a discrete-valued Markov process at the nodes which have access to GNSS measurements. Simulation results show that Markovian modeling and cooperation via factor graph message passing reduce the average 3D root mean square localization error and yield an average vertical position error that meets civil aviation standards for approach and landing.