Singular spectrum analysis (SSA) is a nonparametric spectral estimation method that decomposes time series signals into interpretable components. With the rise of big time series, the demand for effective and scalable SSA techniques has become increasingly urgent. In this paper, we propose a novel multiway extension of SSA, called higher-order multivariate SSA (HO-MSSA), specifically designed for multivariate and multichannel time series signal analysis via tensor decomposition. HO-MSSA utilizes time-delay embedding and tensor singular value decomposition to transform multichannel time series signals into trajectory tensors, which are then decomposed into elementary components in the Fourier domain, rather than the time domain as in traditional SSA methods. These components are grouped into disjoint subsets using spectral clustering, enabling the reconstruction of the underlying source signals. Experimental results demonstrate that HO-MSSA outperforms state-of-the-art SSA methods in various biomedical applications, including electromyography (EMG), electrocardiography (ECG), and electroencephalogram (EEG) signals. • A novel tensor-based multivariate singular spectrum analysis is introduced. • A new time embedding technique embeds time series into a higher dimension. • Tensor SVD is used to factorize the trajectory tensor of time series. • Spectral clustering detects and groups the underlying time series components.

Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eaves-dropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve’s CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve’s information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramér–Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, under-scoring the effectiveness of the proposed deep learning–driven friendly jamming framework under practical ISAC impairments.

This paper proposes a semi-blind approach for near-field channel estimation and 3D source localization for Extremely Large Antenna Arrays. It considers Uniform Planar Arrays and Cross Arrays as particular cases. Unlike conventional Least-Squares estimators relying solely on predefined pilots, the proposed method exploits unknown data symbols within a subspace framework. Since it does not require any prior knowledge of the propagation model, our proposal achieves improved accuracy and robustness. To reduce computational complexity, a Divide-and-Conquer strategy is adopted, enabling fast and parallelizable subspace estimation. In a multipath single-user scenario with Inter-Symbol Interference, localization parameters (angles and range) are recovered from the first channel tap via a low-complexity least-squares fitting, assuming a line-of-sight path. The extension to Cross Arrays provides a simple specialization of the Uniform Plannar Array approach while drastically reducing the computational load. Simulation results confirm that the proposed method achieves accurate channel estimation and 3D localization with substantial complexity reduction as compared to the state of the art.

This paper proposes a goal-oriented design framework for Integrated Sensing and Communications (ISAC) at the network layer. Devices equipped with dual-functional radar and communication (DFRC) transceivers that jointly perform a sensing task and carry data traffic are considered. The goal of the sensing application is to maximize the classification accuracy, while the network aims to preserve throughput for the enhanced Mobile Broadband (eMBB) service and to respect energy constraints. A cross-layer optimization problem that adapts the lower layer parameters (sensing power and scheduling policy) for maximizing the classification accuracy, while respecting constraints related to the available resources (eMBB throughput, device’s maximal power, and total energy), is formulated. A tractable relaxation of the problem is proposed, yielding an approximately optimal policy that is computable by solving successive convex optimization problems. Our numerical experiments, conducted both on a synthetic Gaussian mixture dataset and on a radar simulation dataset, show the superiority of our goal-oriented scheme policy to classical ISAC schemes.

Federated Learning (FL) is a distributed machine learning paradigm that enables multi-client collaborative training without data leaving local devices, thereby effectively protecting data privacy. However, when faced with challenges such as non-independent and identically distribution (Non-IID) data and external attacks (e.g., gradient leakage attacks and Byzantine attacks), traditional FL methods often encounter convergence difficulty and performance degradation. To alleviate these issues, this paper proposes a decentralized and personalized FL method called PDD-FANs, which employs a robust aggregation mechanism based on generative adversarial networks (GANs) to enhance the model’s robustness when training with Non-IID data and under malicious attack. To validate the performance of PDD-FANs in Non-IID setting, we performed comparative experiments on two benchmark datasets against baseline methods, and we evaluated the ability of PDD-FANs to resist Byzantine attack through an ablation study. The experimental results show that PDD-FANs not only perform better in Non-IID setting but also better maintain the learning capability when subjected to malicious attack.

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems.

With the ever-increasing popularity of blockchain applications, securing blockchain networks plays a critical role in these cyber systems. In this paper, we first study cyberattacks (e.g., flooding of transactions, brute pass) in blockchain networks and then propose an efficient collaborative cyberattack detection model to protect blockchain networks. Specifically, we deploy a blockchain network in our laboratory to build a new dataset including both normal and attack traffic data. The main aim of this dataset is to generate actual attack data from different nodes in the blockchain network that can be used to train and test blockchain attack detection models. We then propose a realtime collaborative learning model that enables nodes in the network to share learning knowledge without disclosing their private data, thereby significantly enhancing system performance for the whole network. The extensive simulation and realtime experimental results show that our proposed detection model can detect attacks in the blockchain network with an accuracy of up to 97%.

This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., brute password and flooding of transactions) of blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain network will serve two purposes, i.e., to generate the real traffic data (including both normal data and attack data) for our learning models and to implement real-time experiments to evaluate the performance of our proposed intrusion detection framework. To the best of our knowledge, this is the first dataset that is synthesized in a laboratory for cyberattacks in a blockchain network. We then propose a novel collaborative learning model that allows efficient deployment in the blockchain network to detect attacks. The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, learn the knowledge from data using the Deep Belief Network, and then share the knowledge learned from its data with other blockchain nodes in the network. In this way, we can not only leverage the knowledge from all the nodes in the network but also do not need to gather all raw data for training at a centralized node like conventional centralized learning solutions. Such a framework can also avoid the risk of exposing local data’s privacy as well as excessive network overhead/congestion. Both intensive simulations and real-time experiments clearly show that our proposed intrusion detection framework can achieve an accuracy of up to 98.6% in detecting attacks.

This paper deals with the problem of blind and semi-blind subspace-based channel estimation, when considering MIMO-OFDM communications systems. The proposed solution offers a reduced computational complexity, mainly by a factor of the number of subcarriers, while guaranteeing accurate channel estimation as compared to state-of-the-art techniques. By exploiting the orthogonality property of the OFDM modulation, covariance matrix and noise subspace are estimated for each subcarrier in a parallel scheme, then a global cost function is minimized to obtain channel coefficients estimates. Besides, conditions for channel identifiability as well as the minimum number of subcarriers to be used for the uniqueness of the solution are investigated with various numerical simulations to corroborate our analysis.

Federated learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet of Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads, and enhance privacy for cyberattack detection systems. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this article, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated data sets of networks to have the same features, thus limiting the efficiency, flexibility, as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning (DL) models, even when their data sets have different features. Extensive experiments on recent real-world cybersecurity data sets show that the proposed framework can improve more than 40% as compared to the state-of-the-art DL-based approaches.

Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however nontrivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analystics perspective. Thus, it is imperative to carry out an overview of tensor tracking to help researchers and practitioners understand its developments and achievements, summarise the current trends and advances, and identify challenging problems. In this article, we provide a contemporary and comprehensive survey on different types of tensor tracking techniques. We particularly categorize the state-of-the-art methods into three main groups: streaming CP decompositions, streaming Tucker decompositions, and streaming decompositions under other tensor formats (i.e., tensor-train, t-SVD, and BTD). In each group, we further divide the existing algorithms into sub-categories based on their main optimization framework and model architectures. Finally, we present several applications, research challenges, open problems, and potential directions of tensor tracking in the future.

We studied the problem of sparse subspace tracking in the high-dimensional regime where the dimension is comparable to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.

In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive least- squares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage. Experiments indicate that the proposed algorithm is capable of adaptive tensor decomposition with a competitive performance evaluation on both synthetic and real data.

Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be efficiently implemented in IoT Industry 4.0. In the system under consideration, we develop smart “filters” which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each filter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can significantly reduce the information disclosure as well as network traffic in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.

Physical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information (CSI) of their channel. Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to-end learning in AE-based FJ.

We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.

This paper introduces a new PARAFAC algorithm for a class of third-order tensors. Particularly, the proposed algorithm is based on subspace estimation and solving a non-symmetrical joint diagonalization problem. To deal with large scale problem, a procedure for overcoming scale and permutation ambiguities is proposed in a parallel computing scheme leading to a significant cost reduction of our method. Performance comparison with some state-of-the-art algorithms produces promising results.

This paper deals with synchronization problem in IEEE 802.11a wireless system. In addition to traditional training sequences, the SIGNAL field of the physical frame can be considered as a new source of information. Indeed the receiver is able to predict the SIGNAL unknown parts relying on the knowledge provided by the CSMA/CA protocol during the negotiation of the transmission medium reservation. The exchanged RtS control frame used jointly with the bit-rate adaptation algorithm to the channel helps the receiver not only to predict the SIGNAL field but also to get information on the channel state. Based on this knowledge, joint MAP channel, time and frequency synchronization algorithm is carried out. Moreover to estimate the residual time offset, a timing metric in frequency domain is performed by minimizing the average of transmission errors in the presence of all channel estimation errors. The performance in terms of probability of synchronization failure is shown to be improved compared to existing algorithms.

Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.

This paper addresses the time synchronization problem in IEEE 802.11a OFDM wireless systems. To enhance the coarse time synchronization mechanism, recent methods exploit not only traditional training sequences as specified by the standard but also additional knowledge available when the Carrier Sense Multiple Access with Collision Avoidance mechanism (CSMA/CA) is triggered. In this case, additional information can be used as training sequences based on the protocol knowledge training sequence known by the receiver. This step is followed by a time synchronization and channel estimation which results in the smallest Channel Estimate Errors (CEE) according to the selected criterion (e.g. LS, MAP). However it was found that the synchronization failure probability heavily depends on the channel estimate quality. Therefore to improve the performance of this class of algorithms, we propose an optimal time synchronization metric that minimizes the average of the transmission error over all CEE. Simulation results show a strongly improved performance in terms of synchronization failure probability in comparison with the existing algorithms.

In this paper, we study opportunistic relay selection in cooperative networks with secrecy constraints, where a number of eavesdropper nodes may overhear the source message. To deal with this problem, we consider three opportunistic relay selection schemes. The first scheme tries to reduce the overheard information at the eavesdroppers by choosing the relay having the lowest instantaneous signal-to-noise ratio (SNR) to them. The second scheme is conventional selection relaying that seeks the relay having the highest SNR to the destination. In the third scheme, we consider the ratio between the SNR of a relay and the maximum among the corresponding SNRs to the eavesdroppers, and then select the optimal one to forward the signal to the destination. The system performance in terms of probability of non-zero achievable secrecy rate, secrecy outage probability and achievable secrecy rate of the three schemes are analyzed and confirmed by Monte Carlo simulations.

The estimation of the scattering function in a time-frequency selective fading mobile environment is considered. The scattering function explicitly reveals the time-frequency selective behavior of the fading channel under the well-known wide-sense stationary Gaussian process with uncorrelated scattering (WSSUS) assumption. We propose two classes of estimators based on a time-frequency framework that generalize the existing estimators while giving an extra freedom according to different criteria wanted to be achieved in the estimation of the scattering function. Instead of using the Woodward ambiguity function or symmetric ambiguity function, we use the generalized ambiguity function which comes from the general class of quadratic time-frequency distributions.