IEEE Transactions on Signal Processing 
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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.UL-DL Duality for Cell-Free Massive MIMO With Per-AP Power and Information Constraints
We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information constraints model limited sharing of channel state information and data bearing signals across the transmitters. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint and form its transmit signal using limited cooperation capabilities. Our duality principle applies to ergodic achievable rates given by the popular hardening bound, and it can be interpreted as a nontrivial g...
Source: IEEE Transactions on Signal Processing - March 13, 2024 Category: Biomedical Engineering Source Type: research
Learning Spatiotemporal Graphical Models From Incomplete Observations
This paper investigates the problem of learning a graphical model from incomplete spatio-temporal measurements. Our purpose is to analyze a time-varying graph signal represented by an incomplete data matrix, the rows and columns of which correspond to spatial and temporal features/measurements of the signal, respectively. In contrast to the conventional approaches which utilize either a directed or an undirected graphical model for data analysis, we propose a compound multi-relational model exploiting both directed and undirected structures. Our approach is based on statistical inference in which a spatio-temporal signal i...
Source: IEEE Transactions on Signal Processing - March 13, 2024 Category: Biomedical Engineering Source Type: research
Message Passing Based Wireless Multipath SLAM With Continuous Measurements Correction
The core of multipath-based simultaneous localization and mapping (SLAM) is to utilize the multipath propagation of signals to simultaneously achieve the estimation of the user and the surrounding environment's states. Existing multipath-based SLAM methods are Bayesian estimators that use the current-snapshot signal as input to update the states. However, the internal correlation of time-varying signals is neglected. To utilize the time sequence information in state estimation, we propose a new Bayesian model considering historical measurements and represent it by a factor graph. Based on the model, we develop BP-CC (Belie...
Source: IEEE Transactions on Signal Processing - March 12, 2024 Category: Biomedical Engineering Source Type: research
Augmented Multi-Subarray Dilated Nested Array With Enhanced Degrees of Freedom and Reduced Mutual Coupling
Sparse linear arrays (SLAs) can be designed in a systematic way, with the ability for underdetermined DOA estimation where a greater number of sources can be detected than that of sensors. In this paper, as the first stage, a new systematic design named multi-subarray dilated nested array (MDNA), whose difference co-array (DCA) can be proved to be hole-free, is firstly proposed by introducing a sparse ULA and multiple identical dense ULAs with appropriate sub-ULA spacings. The MDNA will degenerate into the nested array under specific conditions, and the uniform degrees of freedom (uDOFs) of MDNA is larger than that of its ...
Source: IEEE Transactions on Signal Processing - March 12, 2024 Category: Biomedical Engineering Source Type: research
A Tensor Based Varying-Coefficient Model for Multi-Modal Neuroimaging Data Analysis
All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data structure of many of these modalities can be expressed as time-varying multidimensional arrays (tensors), collected at different time-points on multiple subjects. Here, we consider a new approach for the study of neural correlates in the presence of tensor-valued brain i...
Source: IEEE Transactions on Signal Processing - March 11, 2024 Category: Biomedical Engineering Source Type: research
Chaotic Convergence of Newton's Method
In 1680 Newton proposed an algorithm for finding roots of polynomials. His method has since evolved but the core concept remains intact. The convergence of Newton's Method has been widely challenged to be unstable or even chaotic. Here we briefly review this evolution, and consider the question of stable convergence. Newton's method may be applied to any complex analytic function, such as polynomials. Its derivation is based on a Taylor series expansion in the Laplace frequency $s=\sigma+\jmath\omega$. The convergence of Newton's method depends on the Region of Convergence (RoC). Under certain conditions, nonlinear (NL) li...
Source: IEEE Transactions on Signal Processing - March 11, 2024 Category: Biomedical Engineering Source Type: research
Geometrically-Regularized Fast Independent Vector Extraction by Pure Majorization-Minimization
We propose computationally efficient algorithms for extracting a single source of interest (SOI) using geometrically-regularized independent vector extraction (GR-IVE). Conventional GR-IVE relies on a block majorization-minimization (block MM) algorithm, which successively optimizes each part (block) of the separation matrix based on the minimization of a surrogate function. We here extend the block MM to a pure MM by developing a global optimization algorithm for minimizing the same surrogate function when the number of SOI is one. Our global optimization is reduced to finding the minimum solution of the so-called secular...
Source: IEEE Transactions on Signal Processing - March 11, 2024 Category: Biomedical Engineering Source Type: research
On a Novel Time-Varying Up-Sampling Rate (TVUSR) Structure and Its Statistical Properties
Several real-world signals exhibit semi-periodicity in that the period of repetition varies from pulse to pulse about a mean value instead of being constant. Some examples, including among others, are ECG signals, voiced phonemes in speech, carrier jitter in communication etc. In order to model/generate such signals, one can pass a train of discrete-time delta functions, having zeros between two ones in a semi-periodic manner, through an LTI system with the impulse response matched to the pulse. Strictly speaking, the number of zeros between two ones should be a random variable, with the mean value being an average period ...
Source: IEEE Transactions on Signal Processing - March 11, 2024 Category: Biomedical Engineering Source Type: research
Scattering and Gathering for Spatially Varying Blurs
A spatially varying blur kernel $h(\mathbf{x},\mathbf{u})$ is specified by an input coordinate $\mathbf{u} \mathbf{\in} \mathbb{R}^{2}$ and an output coordinate $\mathbf{x} \mathbf{\in} \mathbb{R}^{2}$. For computational efficiency, we sometimes write $h(\mathbf{x},\mathbf{u})$ as a linear combination of spatially invariant basis functions. The associated pixelwise coefficients, however, can be indexed by either the input coordinate or the output coordinate. While appearing subtle, the two indexing schemes will lead to two different forms of convolutions known as scattering and gathering, respectively. We discuss the origi...
Source: IEEE Transactions on Signal Processing - March 11, 2024 Category: Biomedical Engineering Source Type: research
Hybrid Data-Induced Kalman Filtering Approach and Application in Beam Prediction and Tracking
Beam prediction and tracking (BPT) are key technology for high-frequency communications. Typical techniques include Kalman filtering and Gaussian process regression (GPR). However, Kalman filter requires explicit models of system dynamics, which are challenging to obtain, especially for complicated environments. In contrast, as a data-driven approach, there is no need to derive the system dynamics model for GPR. However, the computational complexity of GPR is often prohibitive, which makes real-time application challenging. To tackle this issue, we propose a novel hybrid model and data driven approach in this paper, which ...
Source: IEEE Transactions on Signal Processing - March 8, 2024 Category: Biomedical Engineering Source Type: research
Bayesian Inference for Non-Linear Forward Model by Using a VAE-Based Neural Network Structure
In this paper, a Variational Autoencoder (VAE) based framework is introduced to solve parameter estimation problems for non-linear forward models. In particular, we focus on applications in the field of medical imaging where many thousands of model-based inference analyses might be required to populate a single parametric map. We adopt the concept from Variational Bayes (VB) of using an approximate representation of the posterior, and the concept from the VAE of using the latent space representation to encode the parameters of a forward model. Our work develops the idea of mapping between time-series data and latent parame...
Source: IEEE Transactions on Signal Processing - March 6, 2024 Category: Biomedical Engineering Source Type: research
mPage: Probabilistic Gradient Estimator With Momentum for Non-Convex Optimization
The probabilistic gradient estimator (PAGE) algorithm allows switching between vanilla SGD and variance-reduced methods in a flexible probabilistic manner. This motivates us to develop novel momentum-based algorithms for non-convex finite-sum problems. Specifically, we replace SGD with momentum acceleration in PAGE, and the momentum term is integrated in the inner and outer parts of the gradient estimator, named mPAGE-l and mPAGE-O, respectively. Furthermore, we propose a unified algorithmic framework for momentum variants to cover mPAGE-I and mPAGE-O, denoted as mPAGE. For non-convex objectives, we establish a unified ana...
Source: IEEE Transactions on Signal Processing - March 6, 2024 Category: Biomedical Engineering Source Type: research
Percentile Optimization in Wireless Networks—Part II: Beamforming for Cell-Edge Throughput Maximization
Part I of this two-part paper focused on the formulation of percentile problems, complexity analysis, and development of power control algorithms via the quadratic fractional transform (QFT) and logarithmic fractional transform (LFT) for sum-least-$q^{\mathrm{th}}$-percentile (SLqP) rate maximization problems. In this second part, we first tackle the significantly more challenging problems of optimizing SLqP rate via beamforming in a multiuser, multiple-input multiple-output (MU-MIMO) network to maximize cell-edge throughput. To this end, we first propose an adaptation of the QFT algorithm presented in Part I that enables ...
Source: IEEE Transactions on Signal Processing - March 4, 2024 Category: Biomedical Engineering Source Type: research
Multi-Timescale Ensemble $Q$-Learning for Markov Decision Process Policy Optimization
Reinforcement learning (RL) is a classical tool to solve network control or policy optimization problems in unknown environments. The original $Q$-learning suffers from performance and complexity challenges across very large networks. Herein, a novel model-free ensemble reinforcement learning algorithm which adapts the classical $Q$-learning is proposed to handle these challenges for networks which admit Markov decision process (MDP) models. Multiple $Q$-learning algorithms are run on multiple, distinct, synthetically created and structurally related Markovian environments in parallel; the outputs are fused using an adapti...
Source: IEEE Transactions on Signal Processing - March 4, 2024 Category: Biomedical Engineering Source Type: research
Optimal Transport Based Impulse Response Interpolation in the Presence of Calibration Errors
Acoustic impulse responses (IRs) are widely used to model sound propagation between two points in space. Being a point-to-point description, IRs are generally estimated based on input-output pairs for source and sensor positions of interest. Alternatively, the IR at an arbitrary location in space may be constructed based on interpolation techniques, thus alleviating the need of densely sampling the space. The resulting IR interpolation problem is of general interest, e.g., for imaging of subsurface structures based on seismic waves, rendering of audio and radar IRs, as well as for numerous spatial audio applications. A com...
Source: IEEE Transactions on Signal Processing - March 1, 2024 Category: Biomedical Engineering Source Type: research
