Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates for decentralized tasks. In these scenarios, the underlying graph often changes with time due to link failures or topology variations, creating a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online

Structured compressed sensing takes signal structure into account, thereby outperforming earlier compressed sensing methods. However, results are usually based on sampling in the Fourier domain, such as in Magnetic Resonance Imaging. In the time domain, the benefits of structured compressed sensing are still unknown. This paper introduces concepts that incorporate the signal structure into both the

In this paper, an effective joint radar assignment and power scheduling (JRAPS) scheme is proposed for the multi-target tracking (MTT) task in multi-radar systems (MRSs) under imperfect detection, namely, target related measurements are collected with the probability of detection ($\boldsymbol{P}_{\boldsymbol{D}}$) less than 1. The centralized fusion architecture is adopted by the MRS. Specifically

With the development of deep learning (DL) technology, the feature extraction capability of networks has gradually been enhanced, and high accuracy has been achieved in signal modulation classification (SMC) tasks. DL requires numerous training samples to achieve high classification accuracy. However, in non-cooperative cases, only a few labeled data are usually available. To solve this problem, we

Lippmann (or interferential) photography is the first and only analog photography method that can capture the full color spectrum of a scene in a single take. This technique, invented more than a hundred years ago, records the colors by creating interference patterns inside the photosensitive plate. Lippmann photography provides a great opportunity to demonstrate several fundamental concepts in signal

How to perfectly recover a binary signal from its discrete Fourier transform (DFT) coefficients is studied. The theoretic lower bound and a practical recovery strategy are derived and developed. The concept of ambiguity pair is introduced. This pair of signals has almost the same DFT coefficients except for some positions. It can prove that when the signal length is $N$, then at least $\tau (N)$ DFT

Pulse-Doppler radar systems with medium/high pulse repetition frequencies (PRFs) usually produce severe range ambiguities, and strong nearby ground/sea clutter will then fold into a single range interval with weak remote target echoes, further limiting the dynamic range of radar. The existing methods for modeling or estimating clutter spectra are usually not applicable to the case of folded clutter

This paper presents two novel Bayesian learning recovery algorithms for block sparse signals corresponding to two distinct cases. In the first case, the signals within each block are correlated and the block borders are known, whereas in the second case, the block borders are unknown and the signal elements are uncorrelated. For the first case, the proposed recovery algorithm differs from existing

Recently, the matrix information geometry (MIG) detectors have been speedily developed and demonstrated satisfactory performance in numerous applications. However, the MIG detector with a specific geometric measure is not always satisfactory under changing environments. This paper proposes the adaptive MIG detector with local metric tensor to match the detection environment for improving the adaptability

Non-negative matrix factorization with transform learning (TL-NMF) is a recent idea that aims at learning data representations suited to NMF. In this work, we relate TL-NMF to the classical matrix joint-diagonalization (JD) problem. We show that, when the number of data realizations is sufficiently large, TL-NMF can be replaced by a two-step approach—termed as JD+NMF—that estimates the transform through

This paper studies streaming optimization problems with objectives given by a sum of locally coupled cost terms of the form $f(vx_{t-1},vx_t)$. In particular, we are interested in how the solution $\hat{\boldsymbol{x}}_{t|T}$ for the $t$th frame of variables changes as $T$ increases. While incrementing $T$ and adding a new functional and a new set of variables does in general change the solution everywhere

In this paper, our goal is to investigate local chirp rate (CR) estimation in noisy multicomponent signals. The focus is put on improving a specific type of local CR estimator used in the second order synchrosqueezing transform, which proves to be very inaccurate in the presence of noise. More precisely, the noise creates spurious oscillations in the local CR estimate, and we first put the emphasis

It has been widelydocumented that the sampling and resampling steps in particle filters cannot be differentiated. The reparameterisation trick was introduced to allow the sampling step to be reformulated into a differentiable function. We extend the reparameterisation trick to include the stochastic input to resampling therefore limiting the discontinuities in the gradient calculation after this step

Low multilinear rank approximation (LMLRA) is a tool for compressing a tensor into a more compact form, while preserving most of its information. An LMLRA of an $N$th order tensor consists of $N$ factor matrices which are combined using a core tensor of smaller dimensions than the original tensor. When the tensor is non-static, for example when over time new tensor slices are being added along a certain

Federated learning (FL) has become a hot research area in enabling the collaborative training of machine learning models among multiple clients that hold sensitive local data. Nevertheless, unconstrained federated optimization has been studied mainly using stochastic gradient descent (SGD), which may converge slowly, and constrained federated optimization, which is more challenging, has not been investigated

This paper explores fractional spectrum analysis for periodically and nonuniformly sampled signals with finite disjoint bands in the fractional Fourier domain. Considering that spectrum aliasing occurs in sub-Nyquist sampling in the fractional Fourier domain, we study the conditions for reconstructing the fractional spectrum and devise relevant reconstruction methods. To obtain these goals, we investigate

Multifractal analysis has become a reference tool for signal and image processing. Grounded in the quantification of local regularity fluctuations, it has proven useful in an increasing range of applications, yet so far involving only univariate data (scalar valued time series or single channel images). Recently the theoretical ground for multivariate multifractal analysis has been devised, showing

In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures–arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure. We model each agent’s state over time as a two-state Markov chain that indicates Byzantine or trustworthy behaviors at different time instants. We set no restrictions on the maximum

Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for various purposes, from hybrid beamforming to strong interference mitigation. These applications require the phases of the PSN elements to be precisely calibrated, but the phase deviations always exist in practice. This paper proposes a novel over-the-air (OTA) approach to estimate the deviations of the phase

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix. To resolve this issue, we introduce a new method for accelerating SBL inference

We consider the classical problem of missing-mass estimation, which deals with estimating the total probability of unseen elements in a sample. The missing-mass estimation problem has various applications in machine learning, statistics, language processing, ecology, sensor networks, and others. The naive, constrained maximum likelihood (CML) estimator is inappropriate for this problem since it tends

Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, which forms the bedrock of modern machine learning. In this work, we seek to balance the fact that attenuating step-size is required for exact convergence with the fact that constant step-size learns faster to a limiting error. To do so, rather than fixing the mini-batch and the step-size at the outset

Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-agent network. In the considered problem, each agent owns some local information and a local variable for jointly minimizing a cost function, but local variables are coupled by linear constraints. Most of the existing methods

In this paper, we consider the problem of sensing the environment within a wireless cellular framework. Specifically, multiple user equipments (UEs) send sounding signals to one or multiple base stations (BSs) and then a centralized processor retrieves the environmental information from all the channel information obtained at the BS(s). Taking into account the occlusion effect that is common in the

Distributed computing enables large-scale computation tasks to be processed by multiple workers in parallel. However, the randomness of communication and computation delays across the workers causes the straggler effect, which may degrade the delay performance. Coded computation helps to mitigate the straggler effect, but the amount of redundant load and task assignment to the workers should be carefully

In this work, we present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware recursive least-squares (DQA-RLS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Moreover, we

In this paper, we study distributed constrained online convex optimization for a wireless system consisting of a parameter server and multiple agents. Each agent has a local constraint function and a time-varying local loss function, and needs to choose sequential actions based on causal information. The goal of the overall system is to minimize the accumulated total loss of all agents over a time

Locating a target is key in many applications, namely in high-stakes real-world scenarios, like detecting humans or obstacles in vehicular networks. In scenarios where precise statistics of the measurement noise are unavailable, applications require localization methods that assume minimal knowledge on the noise distribution. We present a scalable algorithm delimiting a tight superset of all possible

Irregular repetition slotted aloha (IRSA) is a grant-free random access protocol for massive machine-type communications, in which users transmit replicas of their packet in randomly selected resource blocks within a frame. In this paper, we first develop a novel Bayesian user activity detection (UAD) algorithm for IRSA, which exploits both the sparsity in user activity as well as the underlying structure

This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is approximate message passing (AMP). However, the existing theoretical analysis of AMP-based joint AUD and CE can only be performed with a given fixed point of the

Recently, affine projection algorithm has been extensively studied in the Gaussian noise environment. However, the performance of affine projection algorithm will deteriorate rapidly in the presence of impulsive noise and other non-Gaussian noise. To address this issue, this paper proposes a novel affine projection algorithm based on the complex Gaussian kernel function, called widely linear maximum

This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar produces an angle-range-dependent and time-varying transmit beampattern due to the small frequency increment across its array elements, which provides potential applications in new radar techniques. In this paper, we first establish a FDA-MIMO radar receiver model in the presence of spectral interferences, which focuses on the

We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. This general model encompasses various signals such as the traditional scalar-valued graph signal, multichannel graph signal, and discrete- and continuous-time graph signals, allowing us to build a unified theory of graph random processes

We study the consensus decentralized optimization problem where the objective function is the average of $n$ agents private non-convex cost functions; moreover, the agents can only communicate to their neighbors on a given network topology. The stochastic learning setting is considered in this paper where each agent can only access a noisy estimate of its gradient. Many decentralized methods can solve

Phase-modulated continuous-wave (PMCW) radar systems possess inherent interference mitigation capabilities and can be useful for many civilian applications including autonomous driving. For PMCW radar systems, binary sequences with good autocorrelation properties are used as probing sequences due to their low cost to generate in practical hardware systems. However, the non-linearity (NL) suffered by

Due to their shorter operating range and large bandwidth, automotive radars can resolve many reflections from their targets of interest, mainly vehicles. This calls for the use of extended-target models in place of simpler and more widely-adopted point-like target models. However, despite some preliminary work, the fundamental connection between the radar’s accuracy as a function of the target vehicle

In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of $d$ agents. The objective is an average of $d$ local functions, and each function is privately held by an agent and encodes its data. The agents can only communicate with their neighbors in a collaborative effort to solve this problem. In existing methods, multiple

The low-rank recovery is a powerful tool to restore images from incomplete and corrupted observations. Conventional low-rank recovery techniques employ the reweighted nuclear norm minimization, which requires performing the full singular value decomposition and thus is computationally expensive. Using the scheme of bilinear factorization, we propose the Reweighted Low-rank Matrix Factorization (RLMF)

In this paper, we propose FadeLoc a novel method for localizing smart devices in an Internet of Things (IoT) environment, based on the Received Signal Strength (RSS), and a generic ${\kappa {-} \mu}$ fading model where $\kappa$ and $\mu$ denote the fading parameters. The RSS-based localization is challenging because of noise, fading, and non-line-of-sight (NLOS) effects, thus necessitating an appropriate

In this paper, a new framework for continuous-time maximum a posteriori estimation based on the Chebyshev polynomial optimization (ChevOpt) is proposed, which transforms the nonlinear continuous-time state estimation into a problem of constant parameter optimization. Specifically, the time-varying system state is represented by a Chebyshev polynomial and the unknown Chebyshev coefficients are optimized

Many traditional data transmission schemes over wireless communication channels heavily rely on the assumption that channel state information is available. However, for time-varying channels it is not possible to reliably decide the validity of the previously performed channel estimate. Therefore, message transmission methods taking the time-varying nature of the communication channel into account

The multi-reference alignment (MRA) problem entails estimating an image from multiple noisy and rotated copies of itself. If the noise level is low, one can reconstruct the image by estimating the missing rotations, aligning the images, and averaging out the noise. While accurate rotation estimation is impossible if the noise level is high, the rotations can still be approximated, and thus can provide

Suppose an undirected graph is observed over time. Its structure (i.e., nodes and edges) remains the same but the measurements taken at the nodes may vary over time. This paper proposes a method that simultaneously performs the following two tasks: (i) it detects change points in the time domain, and (ii) for each time interval between any two consecutive detected change points, it partitions the nodes

The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser

Network management is a fundamental ingredient for efficient operation of wireless networks. With increasing bandwidth, number of antennas and number of users, the amount of information required for network management increases significantly. Therefore, distributed network management is a key to efficient operation of future networks. This paper focuses on the problem of distributed joint beamforming

A fundamental algorithm for data analytics at the edge of wireless networks is distributed principal component analysis (DPCA), which finds the most important information embedded in a distributed high-dimensional dataset by distributed computation of a reduced-dimension data subspace, called principal components (PCs). In this paper, to support one-shot DPCA in wireless systems, we propose a framework

In this paper, the recursive quadratic filtering issue is addressed for a class of linear discrete-time systems with non-Gaussian noises over time-correlated fading channels. The time-correlated fading channel, whose fading coefficient is modeled by a dynamic process subject to non-Gaussian random disturbance, is adopted to better characterize the time-correlation nature of the communication channel

The number of available worker nodes for distributed computing can be limited and varying in wireless networks with noisy channels. However, the conventional straggler mitigation schemes ensure perfect reconstruction only when the number of worker nodes is more than a predetermined number, called the recovery threshold. When the number of worker nodes is less than the recovery threshold, the conventional

Distributed Kalman filtering techniques enable agents of a multiagent network to enhance their ability to track a system and learn from local cooperation with neighbors. Enabling this cooperation, however, requires agents to share information, which raises the question of privacy. This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) that protects local information of agents by

Graph wavelet filters can be used to construct dictionaries of atoms, and signals defined over graphs can be represented as linear combinations of these atoms. These representations can facilitate the development of various signal processing tasks, e.g. denoising or feature extraction. We present here a method to construct these filters, using mappings on simple prototype filters, that readily allows

Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameters estimated by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, all estimation algorithm used in this context have performance bounds in terms of the achievable mean squared error, which are not exceedable. These bounds depend

Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem

This correspondence disproves the main results in the paper “Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection” by S. Mollaebrahim and B. Beferull-Lozano. Counterexamples and counterproofs are provided when applicable. A correction is suggested for some of the flaws. However, it does not seem possible to amend most of the flaws since the overall approach based on

Carrier phase signals form the basis of various interferometric measurement models and estimation techniques that have parameters of which some are integer. In this paper, integer least-squares estimation theory is extended and applied to bias-bounded mixed-integer models. This extension accommodates the presence of bounded real-valued parameters in mixed-integer models through incorporating prior

Spectral methods include a family of algorithms related to the eigenvectors of certain data-generated matrices. In this work, we are interested in studying the geometric landscape of the eigendecomposition problem in various spectral methods. In particular, we first extend known results regarding the landscape at critical points to larger regions near the critical points in a special case of finding

A sequence set with ambiguity function (AF) specifications is frequently required in multi-transmit active sensing systems which exploit waveform diversity. This paper formulates a new model to jointly design sequence set and mismatched filter bank with AF requirements, which is a generalization of the auto-AF and cross-AF adopted in the matched filter scheme to attain lower AF sidelobe levels with

Recent work in time-frequency analysis proposed to switch the focus from the maxima of the spectrogram toward its zeros, which, for signals corrupted by Gaussian noise, form a random point pattern with a very stable structure leveraged by modern spatial statistics tools to perform component disentanglement and signal detection. The major bottlenecks of this approach are the discretization of the Short-Time

Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design counter-measures. In an earlier work, we proposed to formulate the estimation of the reproduction number as an optimization problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal