IEEE Transactions on Signal Processing

In this paper, we generalize the concepts of kernels, weak stationarity and white noise from undirected to directed graphs (digraphs) based on the Jordan decomposition of the shift operator. We characterize two types of kernels (type-I and type-II) and ...

Steganography is the art of covert communication that pursues the secrecy of concealment. In adaptive steganography, the most commonly used framework of steganography, the sender embeds a “secret message” signal within another “cover&#...

This paper explores constrained non-convex personalized federated learning (PFL), in which a group of workers train local models and a global model, under the coordination of a server. To address the challenges of efficient information exchange and ...

We consider a least absolute deviation (LAD) approach to the robust phase retrieval problem that aims to recover a signal from its absolute measurements corrupted with sparse noise. To solve the resulting non-convex optimization problem, we propose a ...

Communication base stations can achieve high-precision tracking and accurate classification for multiple extended targets in the context of integrated communication and sensing by transmitting wideband signal. However, the time resources of the base ...

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing ...

Graph Neural Networks (GNNs) have exhibited exceptional performance across diverse application domains by harnessing the inherent interconnectedness of data. Recent findings point towards instability of GNN under both feature and structure perturbations. ...

A leading family of algorithms for state estimation in dynamic systems with multiple sub-states is based on particle filters (PFs). PFs often struggle when operating under complex or approximated modelling (necessitating many particles) with low latency ...

Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often ...

This article addresses the problem of computing a Cramér-Rao bound when the likelihood of Euclidean observations is parameterized by both unknown Lie group (LG) parameters and covariance matrix. To achieve this goal, we leverage the LG structure of ...

We study the sparse recovery problem with an underdetermined linear system characterized by a Kronecker-structured dictionary and a Kronecker-supported sparse vector. We cast this problem into the sparse Bayesian learning (SBL) framework and rely on the ...

This paper proposes a Gaussian-Cauchy mixture maximum correntropy criterion Kalman filter algorithm (GCM_MCCKF) for robust state estimation in linear systems under non-Gaussian noise, particularly heavy-tailed noise. The performance of the MCCKF depends ...

The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is ...

In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a ...

This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) ...

The sampling interval generated by a local clock (biological, physical, or digital) is known to have a certain amount of errors (deterministic or random) called timing jitter. The latter can vary in nature and magnitude depending on how accurately the ...

Joint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. “datasets”, into “sources” that are statistically dependent across datasets and independent within datasets. Despite this usefulness for ...

In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal ...

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of <inline-formula><tex-math notation="...

Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many ...

This paper deals with frequency domain parametric identification of ARMAX models when the input is corrupted by white noise. By means of a multivariate ARMA representation, the ARMAX model within the errors-in-variables (EIV) framework is identified by a ...

This paper addresses the multi-object localization problem by using a hybrid of differential time delay (DTD) and angle-of-arrival (AOA) measurements collected by a single receiver in an unsynchronized multistatic localization system, where two kinds of ...

Directly parameterizing and learning gradients of functions has widespread significance, with specific applications in inverse problems, generative modeling, and optimal transport. This paper introduces gradient networks (<monospace>GradNets</monospace>): ...

The sensor position uncertainties and synchronization offsets can cause substantial performance degradation if the sensors are not properly calibrated. This paper investigates the localization of a constant velocity moving target and the self-calibration ...

In this paper, the channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of near-field (NF) spherical wavefront effects and spatially non-stationary (SnS) properties. Due to ...