IEEE Transactions on Signal Processing This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems
In this paper, we propose a novel framework that aims to jointly design the reflection coefficients of multiple reconfigurable intelligent surfaces and the precoding strategy of a single base station (BS) to optimize the self-tracking of the position and the velocity of a single multi-antenna user equipment (UE) that moves either in the far- or near-field region. Differently from the literature, and to keep the overall complexity affordable, we assume that RIS optimization is performed less frequently than localization and precoding adaptation. The proposed procedure leads to minimize the inverse of the received power in t...
Source: IEEE Transactions on Signal Processing - September 19, 2023 Category: Biomedical Engineering Source Type: research

Byzantine-Resilient Decentralized Stochastic Optimization With Robust Aggregation Rules
This article focuses on decentralized stochastic optimization in the presence of Byzantine attacks. During the optimization process, an unknown number of malfunctioning or malicious workers, termed as Byzantine workers, disobey the algorithmic protocol and send arbitrarily wrong messages to their neighbors. Even though various Byzantine-resilient algorithms have been developed for distributed stochastic optimization with a central server, we show that there are two major issues in the existing robust aggregation rules when being applied to the decentralized scenario: disagreement and non-doubly stochastic virtual mixing ma...
Source: IEEE Transactions on Signal Processing - September 19, 2023 Category: Biomedical Engineering Source Type: research

Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity to deal with a large amount of data observed with uncertainties. An exemplar special case of SA pertains to the popular stochastic (sub)gradient algorithm which is the working horse behind many important applications. A lesser-known fact is that the SA scheme also extends to non-stochastic-gradient algorithms such as compressed stochastic gradient, stochastic expectation-maximization, and a number of reinforcement learning algorithms. The aim of ...
Source: IEEE Transactions on Signal Processing - September 18, 2023 Category: Biomedical Engineering Source Type: research

Deep Learning Aided State Estimation for Guarded Semi-Markov Switching Systems With Soft Constraints
This article investigates the problem of state estimation for guarded semi-Markov switching systems with soft constraints. We first construct the switching system in a multiple model manner and derive the recursive Bayesian filter compatible with the sojourn time and base state dependent mode transitions. To solve the intractable conditioned mode transition probabilities, we develop deep learning based classifiers with long short-term memory networks capturing the temporal dependencies. Various network structures are designed to handle different situations where knowledge of the transition probability matrix is available o...
Source: IEEE Transactions on Signal Processing - September 18, 2023 Category: Biomedical Engineering Source Type: research

Asynchronous Distributed Beamforming Optimization Framework for RIS-Assisted Wireless Communications
Reconfigurable intelligent surface (RIS) is a promising solution to enhance the spectral and energy efficiencies of future wireless networks. In this paper, we aim to maximize the sum rate of the RIS-assisted multiuser system with different availabilities of channel state information (CSI) by jointly optimizing the transmit precoding matrix and the RIS reflection matrix. Considering the large-scale nature of the RIS and the potential large number of served users, the conventional centralized optimization framework suffers from huge computational and communication overheads, and does not scale well with the system size. To ...
Source: IEEE Transactions on Signal Processing - September 18, 2023 Category: Biomedical Engineering Source Type: research

Proximal Alternating Partially Linearized Minimization for Perturbed Compressive Sensing
In this paper, we consider a broad class of nonconvex and nonsmooth composition optimization problems that can be used to model many applications in signal processing and image processing, such as sparse signal recovery and image restoration. However, due to the nonconvex nonsmooth properties of the objective function, solving this class of problems using classical methods like alternating minimization will face challenges in theoretical analysis and numerical calculation. For this, we propose a proximal alternating partially linearized minimization (PAPLM) algorithm by linearizing the nonconvex term and combining it with ...
Source: IEEE Transactions on Signal Processing - September 18, 2023 Category: Biomedical Engineering Source Type: research

L${}_{\text{2}}$min${}^{\text{2/2s}}$: Efficient Linear Reconstruction Filter for Incremental Delta-Sigma ADCs
While it becomes more challenging to improve the energy efficiency of incremental delta-sigma data converters (IDCs) from the analog circuit design perspective, we propose two novel linear reconstruction filters for IDCs to enhance their performance in a digital way, including the L${}_{\mathbf{2}}$min${}^{\mathbf{2}}$ filter and its symmetric version, the L${}_{\mathbf{2}}$min${}^{\mathbf{2s}}$ filter. Compared to the classical linear reconstruction filters, such as the cascade-of-integrators (CoI) and cascaded integrator-comb (CIC) filter (an implementation of sinc filter), the proposed filters can achieve efficient quan...
Source: IEEE Transactions on Signal Processing - September 18, 2023 Category: Biomedical Engineering Source Type: research

Inverse Extended Kalman Filter—Part II: Highly Nonlinear and Uncertain Systems
Counter-adversarial system design problems have lately motivated the development of inverse Bayesian filters. For example, inverse Kalman filter (I-KF) has been recently formulated to estimate the adversary’s Kalman-filter-tracked estimates and hence, predict the adversary’s future steps. The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF). The companion paper proposed the theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs). In this paper, we develop this...
Source: IEEE Transactions on Signal Processing - September 15, 2023 Category: Biomedical Engineering Source Type: research

Inverse Extended Kalman Filter—Part I: Fundamentals
Recent advances in counter-adversarial systems have garnered significant research attention to inverse filtering from a Bayesian perspective. For example, interest in estimating the adversary’s Kalman filter tracked estimate with the purpose of predicting the adversary’s future steps has led to recent formulations of inverse Kalman filter (I-KF). In this context of inverse filtering, we address the key challenges of non-linear process dynamics and unknown input to the forward filter by proposing an inverse extended Kalman filter (I-EKF). The purpose of this paper and the companion paper (Part II) is to develo...
Source: IEEE Transactions on Signal Processing - September 15, 2023 Category: Biomedical Engineering Source Type: research

Upper Bound of Null Space Constant $rho(p,t,A,k)$ and High-Order Restricted Isometry Constant $delta_{tk}$ for Sparse Recovery via $ell_{p}$ Minimization
The $boldsymbol{ell_{p}}$ null space property ($boldsymbol{ell_{p}}$-NSP) and restricted isometry property (RIP) are two important frames for sparse signal recovery. New sufficient conditions in terms of $boldsymbol{ell_{p}}$-NSP and RIP are respectively developed in this paper. Firstly, we characterize the $boldsymbol{ell_{p}}$ robust null space property ($boldsymbol{ell_{p}}$-RNSP) concerning two high-order restricted isometry constants. Then we derive an upper bound of $boldsymbol{ell_{p}}$-NSC $boldsymbol{rho(p,t,A,k)}$ for the exact recovery of $boldsymbol{k}$-sparse signals via $boldsymbol{ell_{p}}$ minimization. Sec...
Source: IEEE Transactions on Signal Processing - September 15, 2023 Category: Biomedical Engineering Source Type: research

Alternating Direction Method of Multipliers Based on $\ell_{2,0}$-Norm for Multiple Measurement Vector Problem
The multiple measurement vector (MMV) problem is an extension of the single measurement vector (SMV) problem, and it has many applications. Nowadays, most studies of the MMV problem are based on the $\boldsymbol{\ell}_{\mathbf{2,1}}$-norm relaxation, which will fail in recovery under some adverse conditions. We propose an alternating direction method of multipliers (ADMM)-based optimization algorithm to achieve a larger undersampling rate for the MMV problem. The key innovation is the introduction of an $\boldsymbol{\ell}_{\mathbf{2,0}}$-norm sparsity constraint to describe the joint-sparsity of the MMV problem; this diffe...
Source: IEEE Transactions on Signal Processing - September 15, 2023 Category: Biomedical Engineering Source Type: research

A Unified Framework for Solving a General Class of Nonconvexly Regularized Convex Models
This article proposes a general class of such convexity-preserving (CP) regularizers, termed partially smoothed difference-of-convex (pSDC) regularizer. The pSDC regularizer is formulated as a structured difference-of-convex (DC) function, where the landscape of the subtrahend function can be adjusted by a parameterized smoothing function so as to attain overall-convexity. Assigned with proper building blocks, the pSDC regularizer reproduces existing CP regularizers and opens the way to a large number of promising new ones. With respect to the resultant nonconvexly regularized convex (NRC) model, we derive a series of over...
Source: IEEE Transactions on Signal Processing - September 14, 2023 Category: Biomedical Engineering Source Type: research

Time-Varying Phase Noise Estimation, Channel Estimation, and Data Detection in RIS-Assisted MIMO Systems via Tensor Analysis
In this article, we propose a nested tensor-based framework for the time-varying phase noise (PHN) estimation, channel estimation, and data detection in downlink reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems. Due to the structure of time-varying PHN and RIS phase shifts, we construct the received signal at the mobile station (MS) as a fourth-order tensor. By designing the multi-blocks time-domain transmission scheme, this fourth-order tensor can be converted into a third-order nested tensor model to facilitate the parallel factor (PARAFAC) decomposition. Based on the recons...
Source: IEEE Transactions on Signal Processing - September 14, 2023 Category: Biomedical Engineering Source Type: research

Sparse Array Beamformer Design via ADMM
In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, we prove that the proposed algorithm converges to the set of Karush-Kuhn-Tucker stationary points. Numerical results exhibit its ...
Source: IEEE Transactions on Signal Processing - September 14, 2023 Category: Biomedical Engineering Source Type: research

On Ordered Transmission Based Distributed Gaussian Shift-in-Mean Detection Under Byzantine Attacks
The ordered transmission based (OT-based) schemes reduce the number of transmissions needed in a distributed detection network without any loss in the probability of error performance. In this paper, we investigate the performance of a conventional OT-based system in the presence of additive Byzantine attacks in Gaussian shift in mean problems. In this work, by launching additive Byzantine attacks, attackers are able to alter the order as well as the data for the binary hypothesis testing problem. We also determine the optimal attack strategy for the Byzantine sensors. Furthermore, we analyze a communication efficient OT-b...
Source: IEEE Transactions on Signal Processing - September 14, 2023 Category: Biomedical Engineering Source Type: research