In software-defined satellite information networks (SD-SINs), each requested service can be characterized by a predetermined sequence of virtual network functions (VNFs), referred to as a service function chain (SFC). However, VNFs shared by multiple requested services are prone to failures, causing service interruptions. Furthermore, the rapid movement of satellites results in an intermittent yet predictable network topology. Moreover, efficient use of multi-dimensional heterogeneous resources can enhance reliability and network performance. Therefore, in this paper, we investigate reliability-enhanced network slicing by jointly exploiting communication, storage, and computation resources in time-varying SD-SINs. Specifically, we use the time-expanded graph (TEG) to model time-varying SD-SINs with multi-dimensional heterogeneous resources. Based on TEG, we propose a joint reliability-enhanced VNF deployment and flow routing strategy, formulated as an integer nonlinear programming (INLP) problem, to maximize the number of completed services with reliability requirements. To effectively solve the INLP problem, we propose two novel algorithms: the integer linear programming reformulation (ILPR) algorithm, which achieves optimal solutions but with high complexity, and the LP relaxation-based VNF deployment and routing (LPR-VDR) algorithm, which provides near-optimal solutions with significantly lower complexity. Simulation results demonstrate that the LPR-VDR algorithm performs very closely to the ILPR algorithm.

Radio map estimation (RME) is crucial for effective planning and optimization of wireless networks. Traditional approaches such as interpolation excel at capturing local smoothness in densely populated data but struggle with sparse or irregular data. Conversely, matrix completion (MC) approaches utilize global structures but require huge number of samples and may produce non-smooth estimates. To integrate these strengths, we propose a convex optimization approach for RME (IIMC-RME) that merges interpolation with MC. This approach formulates the RME task as a low-rank MC problem constrained by interpolated results. Additionally, we have developed a convergent algorithm utilizing the alternating direction method of multipliers (ADMM) to efficiently solve the IIMC-RME problem. Experimental evaluations on both synthetic and real-world datasets have shown that IIMC-RME surpasses existing approaches, thereby achieving superior accuracy in RME.

Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. Data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach that can effectively incorporate learned information without training over radio map data. Our idea is to employ the plug-and-play (PnP) denoising scheme from computational imaging. The PnP framework allows incorporating denoisers trained over natural images to handle other types of data, e.g., ocean sound fields and medical images, due to the similarity of their denoising processes. Conventional PnP methods mostly use the learned denoisers in the data domain. Instead, the proposed approach applies PnP in the latent domain through a tailored algorithm design and spatial-spectral factorization of radio maps. This way, the proposed RME method exhibits enhanced scalability and noise robustness. Simulations are used to illustrate the effectiveness of the proposed approach.

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems.

In this letter, we look into the emitter localization task within the Direct Position Determination (DPD) paradigm. This paradigm is by essence a largest eigenvalue problem which treats the channel attenuation variables as free parameters. We consider the channel fading physical rule on electromagnetic signal propagation and reformulate the traditional DPD problem with a channel contextual prior. Thereafter, we develop iterative optimization algorithms based on the majorization-minimization (MM) framework. Numerical results show that the proposed algorithms outperform the traditional DPD estimators with better localization performance.

In this paper, we reflect on an interesting question whether direction of arrival (DOA) detection algorithms can be deceived. When the transmit signals are independent, prevalent DOA algorithms suffice to find the correct arriving directions from measurement statistics at the receiver end. But when the signals are coherent, the expected spectrum peaks may not show up and the detection performance deteriorates to a great extent. Instead of addressing the coherence issue, we take advantage of this phenomenon and fake a peak at a desired DOA in an attempt to launch a deception attack. The deception is realized by constructing a specified steering vector through a composite of candidate vectors in the dictionary. Thereafter, we apply the alternating direction method of multipliers (ADMM) framework to develop an efficient solution algorithm. Numerical simulations show that the proposed algorithm gives satisfactory DOA deception performance.

Accurate prediction of mmWave time-varying channels is essential for mitigating the issue ofchannel agingin highly dynamic scenarios. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations.

Cooperative sensing, a technique employed in cognitive radio (CR) networks for spectrum sensing, exhibits promising potential in bolstering spectrum utilization and enhancing network performance. This approach leverages the information captured by distributed CR users, which is subsequently aggregated at a fusion center. However, the challenges arise when the data are transmitted with low-quality, resulting in the consequential issue of missing data. These factors introduce complexity in detecting primary signals and undermine the reliability of cooperative sensing. In this study, we present a significant advancement in cooperative sensing methodologies by introducing a novel approach: a generalized likelihood ratio test (GLRT) type detector specifically designed to be robust to missing data. More specifically, our proposed robust GLRT detector modifies the computation of the classical GLRT test statistic to accommodate the inherent incompleteness of the data and effectively estimates the desired unknown parameters. Through numerical experiments, we demonstrate the resilience and robustness of our proposed cooperative signal detection method.

Decentralized computation has received considerable research interest lately, due to its wide applications in information processing systems. However, one key requirement to establish convergence for almost all decentralized algorithms, for convex and non-convex problems alike, is that the loss function has Lipschitz-continuous gradient (LipGrad). This is a strong assumption, which does not hold for many practical problems, such as matrix/tensor factorization, neural network training, etc. On the contrary, in the centralized setting, one can utilize techniques such as the Bregman proximal gradient (BPG) method to deal with the lack of LipGrad. This work fills the gap between centralized and decentralized cases by developing a novel smoothed decentralized BPG algorithm to deal with a class of nonconvex decentralized problem, where the local problems do not have LipGrad objective functions. By leveraging the recent notion of relative smoothness and primal-dual error bounds, we show that the proposed algorithm achieves a certain ε-stationary solution by using $\mathcal{O}\left( {{\varepsilon ^{ - 2}}} \right)$ iterations, matching the rate of the centralized Bregman proximal gradient method. To our knowledge, this is the first decentralized algorithm that matches the centralized convergence rate bounds under the class of considered problems. Our numerical results on the decentralized quadratic regression example demonstrate the effectiveness of proposed algorithm.

Fair principal component analysis (FPCA), a ubiquitous dimensionality reduction technique in signal processing and machine learning, aims to find a low-dimensional representation for a high-dimensional dataset in view of fairness. The FPCA problem involves optimizing a non-convex and non-smooth function over the Stiefel manifold. The state-of-the-art methods for solving the problem are subgradient methods and semidefinite relaxation-based methods. However, these two types of methods have their obvious limitations and thus are only suitable for efficiently solving the FPCA problem in special scenarios. This paper aims at developing efficient algorithms for solving the FPCA problem in general, especially large-scale, settings. In this paper, we first transform FPCA into a smooth non-convex linear minimax optimization problem over the Stiefel manifold. To solve the above general problem, we propose an efficient alternating Riemannian/projected gradient descent ascent (ARPGDA) algorithm, which performs a Riemannian gradient descent step and an ordinary projected gradient ascent step at each iteration. We prove that ARPGDA can find an ε-stationary point of the above problem within ${\mathcal{O}}\left( {{\varepsilon ^{ - 3}}} \right)$ iterations. Simulation results show that, compared with the state-of-the-art methods, our proposed ARPGDA algorithm can achieve a better performance in terms of solution quality and speed for solving the FPCA problems.

A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio test detector. Cooperative sensing, which makes use of multiple receivers distributed in different locations, has the advantage of being able to make full use of the distributed antennas and enjoy a high spatial diversity gain. However, the successful employment of cooperative sensing depends on the reliable information exchange among the cooperating receivers over a long range, which may be impractical for real-world scenarios. In this paper, we consider the scenario where each receiving node can only broadcast its received raw data in a short-range communication fashion. We propose a novel cooperative sensing scheme by allowing each node to send to the fusion center only local correlation coefficients, computed within a neighborhood. A detection algorithm, based on matrix factorization of the partially received sample covariance matrix, i.e., with missing entries, is proposed. The performance of our proposed cooperative scheme is verified via numerical experiments.

In this paper, we study the classical Logistic Regression (LR) problem in machine learning. Traditionally, the solving algorithms are based on either the first- or second-order approximation of the objective. For instance, the Fixed-Hessian Newton (FHN) method approximates the true Hessian with a constant estimate. In contrast, our design additionally exploits the third-order information. Applying the majorization–minimization (MM) framework, we construct a novel majorizing function based on the third-order Taylor expansion and the minimization solution is in closed-form with perseverance of the true gradient and Hessian structures. In analysis, we prove the convergence rate of the proposed algorithm. The enhanced numerical performance can be verified through simulation results.

Unmanned aerial vehicle (UAV) swarm-based sensing technology has become increasingly important due to its exceptional maneuverability, versatile coverage capabilities, and reliable line-of-sight (LoS) connectivity. However, the sensing accuracy improvement by exploiting resource coordination strategy poses a new challenge on multi-UAV sensing system. In this paper, we consider the problem of cooperative sensing via a system of multi-UAV, where each UAV is equipped with a directional antenna to cooperatively conduct energy detection for several targets of interest. To measure the perception ability of the system, we choose the energy detection probability as the metric, aiming to maximize the sum detection probability of the network by jointly optimizing UAVs’ deployment, as well as the directional antenna orientations. By virtue of the specific problem structure, we recast the formulation into an equivalent yet more tractable form with the aid of auxiliary vectors. Subsequently, we propose an efficient iterative algorithm for the solution based on the alternating direction penalty method (ADPM), which decomposes the formulated non-convex problem into multiple subproblems and solves them alternately. Extensive simulations validate the efficacy of the proposed algorithm and provide valuable insights for practical system design.

In this paper, we consider the problem of cooperative sensing via a system of multi-unmanned aerial vehicles (UAVs), where each UAV is equipped with a directional antenna to cooperatively perform detection tasks for several targets of interest. To measure the perception ability of the system, we choose the detection probability as the metric, aiming to maximize the sum detection probability of targets by jointly optimizing UAVs’ deployment and directional antenna orientations. To tackle the inherent nonconvexity of the formulated problem, we first decompose it into two sub-problems, i.e., a slave problem for optimizing the antenna orientations with a given UAVs’ deployment, and a master problem for optimizing the UAVs’ deployment. By virtue of the slave problem structure, an efficient block coordinate descent (BCD) algorithm is developed. Meanwhile, to deal with the lack of the closed expression of the sum detection probability with respect to the UAVs’ deployment, we further develop an iterative algorithm to acquire an efficient solution with the aid of Gibbs Sampling (GS) approach. Extensive simulations demonstrate the efficacy of the proposed algorithm.

This article considers the 3-D collaborative trajectory optimization (CTO) of multiple unmanned aerial vehicles to improve multitarget tracking performance with an asynchronous angle of arrival measurements. The predicted conditional Cramér–Rao lower bound is adopted as a performance measure to predict and subsequently control tracking error online. Then, the CTO problem is cast as a time-varying nonconvex problem subjected to constraints arising from dynamic and security (height, collision, and obstacle/target/threat avoidance). Finally, a comprehensive solution method (CSM) is presented to tackle the resulting problem, according to its unique structures. Specifically, if all security constraints are inactive, the CTO can be simplified as a nonconvex problem with convex dynamic constraints, which can be solved by the nonmonotone spectral projected gradient (NSPG) method. Oppositely, an alternating direction penalty method (ADPM) is presented to solve the CTO problem with some positive security constraints. The ADPM introduces auxiliary vectors to decouple the complex constraints and separates the CTO into several subproblems and tackles them alternately, while locally adjusting the penalty factor at each iteration. We show the subproblem w.r.t. the position vector is nonconvex but with convex constraints, which can be efficiently solved by the NSPG method. The subproblems w.r.t. the auxiliary vectors are separable and have closed-form solutions. Simulation results demonstrate that the CSM outperforms the unoptimized method in terms of tracking performance. Besides, the CSM achieves the near-optimal performance provided by the genetic algorithm with much lower computational complexity.

In this article, a composed resource optimization (CRO) scheme is developed for an active and passive radar network engaged in multiple target tracking (MTT). The motivation of the CRO scheme is to collaboratively optimize the transmit resources of active radars, as well as the receiving processing resources of passive radars, to improve the overall MTT performance. We utilize the predicted conditional Cramér–Rao lower bound to evaluate the impact of allocation strategies on tracking performance and formulate the CRO as a mixed-integer nonlinear program problem since the adaptable parameters w.r.t. the target selection process are in binary form. To solve the problem, we propose an alternating direction method of multiplier-based algorithm. This algorithm transforms the original problem into an equality constrained problem by introducing two auxiliary vectors. In such a case, the CRO problem can be tackled by alternately solving several simple subproblems. Specifically, the subproblem w.r.t. the resource vector is convex, and the subproblems w.r.t. the auxiliary vectors are separable. Simulation results demonstrate that the proposed CRO scheme outperforms the traditional allocation schemes in terms of MTT performance. In addition, the performance of the CRO scheme is close to the optimal performance provided by the exhaustive method, but the computation load of the CRO scheme is lower than that of the exhaustive method. Finally, physical interpretations are presented to support our conclusions.

Canonical polyadic decomposition (CPD) has been a workhorse for multimodal data analytics. This work puts forth a stochastic algorithmic framework for CPD under β-divergence, which is well-motivated in statistical learning—where the Euclidean distance is typically not preferred. Despite the existence of a series of prior works addressing this topic, pressing computational and theoretical challenges, e.g., scalability and convergence issues, still remain. In this paper, a unified stochastic mirror descent framework is developed for large-scale β-divergence CPD. Our key contribution is the integrated design of a tensor fiber sampling strategy and a flexible stochastic Bregman divergence-based mirror descent iterative procedure, which significantly reduces the computation and memory cost per iteration for various β. Leveraging the fiber sampling scheme and the multilinear algebraic structure of low-rank tensors, the proposed lightweight algorithm also ensures global convergence to a stationary point under mild conditions. Numerical results on synthetic and real data show that our framework attains significant computational saving compared with state-of-the-art methods.

There has been a growing interest in developing data-driven, in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment where parameters such as CSIs keep changing.This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. Specifically, we consider an "episodically dynamic" setting where the environment changes in "episodes", and in each episode the environment is stationary. We propose a continual learning (CL) framework for wireless systems, which can incrementally adapt the learning models to the new episodes, without forgetting models learned from the previous episodes. Our design is based on a novel min-max formulation which ensures certain "fairness" across different episodes. Finally, we demonstrate the effectiveness of the CL approach by customizing it to a popular DNN based model for power control, and testing using both synthetic and real data.

Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional hearing aids beamforming algorithms require the a priori knowledge of the auditory of the listener, which may not be available in real applications. Recent advances in electroencephalography (EEG) offer a potential non-invasive solution to this problem. The listener's auditory is derived from the EEG signals through auditory decoding algorithms and can be used as an input to the beamforming algorithms. In [1], a joint auditory decoding and adaptive beamforming algorithm framework by correlating the envelope of beamforming output and the EEG signal was proposed to improve the beamformer's robustness against decoding error. Consistent performance improvement was demonstrated on an EEG database recorded on listeners with fixed . In this study, we present the evaluation results of this joint formulation on a new EEG dataset collected on subjects with dynamic switch. We demonstrate not only the joint framework's performance improvement against decoding errors, but also its ability to capture listener's dynamic switch.

Traditional adaptive binaural beamforming algorithms for hearing devices often assume that the target talker is known or can be derived from the listener's look direction. When this assumption is violated, the traditional beamforming algorithms often produce distorted target speech and less than optimal noise and interference suppression. Recent advances in electroencephalography (EEG) and its applications to auditory attention decoding have offered a potential solution for tracking the listeners auditory attention in a multi-talker environment [2]-[5]. In this paper, we propose a unified model for joint auditory attention decoding and adaptive binaural beamforming, and solve the problem using an iterative optimization approach. The proposed algorithm has two advantages over the existing algorithms. First, the optimization objective aims to balance auditory attention alignment, target speech distortion, noise and interference suppression. Secondly, there is no need to estimate the speech envelope of each talker from the noisy and reverberant mixture which is a very challenging problem in practice. The proposed algorithm was evaluated using a newly recorded EEG database for a multi-talker, noisy and reverberant environment [6]. The evaluation results confirm the benefits of the proposed algorithm.

Waveform optimization is a crucial step in the design of a multiple-input multiple-output system. This letter considers the joint optimization of constant modulus waveforms and mismatched (or matched) receive filters to suppress the auto- and cross correlations using the minimax ( $\ell _{\infty }$ -norm) design criterion. For practical waveform length and system size, the waveform design problem becomes quite challenging due to the large problem size (more than $10^5$ unimodular complex variables and $10^7$ nonlinear constraints). In addition to the large size, this problem is nonconvex, nonsmooth, and as such, cannot be handled effectively by the existing waveform design algorithms or off-the-shelve optimization tools. This letter develops an efficient primal–dual type algorithm with low per-iteration complexity to solve this problem. Numerical comparison shows that the waveforms based on the minimax design outperform those obtained from the existing $\ell _2$ -norm design by 4–5 dBs in terms of peak sidelobe levels.

Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional beamforming algorithms such as linearly constrained minimum variance (LCMV) beamformer cannot effectively suppress multiple interferences when the degree of freedom (DoF) of the array is less than the number of sources in the environment. In [1], a penalized inequality-constrained minimum variance (P-ICMV) beamformer was proposed to address this challenge. In this study, we evaluate the P-ICMV beamformer and compare its performance with other beamformers including the LCMV in a multiple-interference environment. In an objective evaluation, objective metrics related to speech intelligibility and sound quality are used to compare the algorithm performance. In a subjective evaluation, the speech intelligibility of the beamformer processed stimuli are evaluated using normal-hearing listeners. Both the objective and subjective evaluation results show that the P-ICMV beamformer can suppress the interferences more effectively than the existing beamformers when the array DoF is limited.

Beamforming algorithms in binaural hearing aids are crucial to improve speech understanding in background noise for hearing impaired persons. In this study, we compare and evaluate the performance of two recently proposed minimum variance (MV) beamforming approaches for binaural hearing aids. The binaural linearly constrained MV (BLCMV) beamformer applies linear constraints to maintain the target source and mitigate the interfering sources, taking into account the reverberant nature of sound propagation. The inequality constrained MV (ICMV) beamformer applies inequality constraints to maintain the target source and mitigate the interfering sources, utilizing estimates of the direction of arrivals (DOAs) of the target and interfering sources. The similarities and differences between these two approaches is discussed and the performance of both algorithms is evaluated using simulated data and using real-world recordings, particularly focusing on the robustness to estimation errors of the relative transfer functions (RTFs) and DOAs. The BLCMV achieves a good performance if the RTFs are accurately estimated while the ICMV shows a good robustness to DOA estimation errors.

An important step in multi-sensor data fusion is sensor registration, namely, to estimate sensors' range and azimuth biases from their asynchronous measurements. Assuming the target moves in a straight line with an unknown constant velocity, we propose a two-stage nonlinear least square (LS) approach to this problem. More specifically, in stage I, each sensor first estimates its own range bias individually, and then in stage II, all sensors jointly estimate their azimuth biases. We show that both of the nonconvex LS problems can be solved to global optimality under mild conditions. Simulation results show that the root mean square error (RMSE) of the proposed approach is quite close to the Cramér-Rao lower bound (CRLB) when the level of the measurement noise is small.