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.

Randomized Iterative Sampling Decoding Algorithm For Large-Scale MIMO Detection
In this paper, the paradigm of the traditional iterative decoding schemes for the uplink large-scale MIMO detection is extended by sampling in an Markov chain Monte Carlo (MCMC) way. Different from iterative decoding whose performance is upper bounded by the suboptimal linear decoding scheme like ZF or MMSE, the proposed iterative random sampling decoding (IRSD) algorithm is capable of achieving the optimal ML decoding performance with the increment of Markov moves, thus establishing a flexible trade-off between suboptimal and optimal decoding performance. According to convergence analysis, we show that the Markov chain in...
Source: IEEE Transactions on Signal Processing - January 4, 2024 Category: Biomedical Engineering Source Type: research

One-Bit Spectrum Sensing for Cognitive Radio
Spectrum sensing for cognitive radio requires effective monitoring of wide bandwidths, which translates into high-rate sampling. Traditional spectrum sensing methods employing high-precision analog-to-digital converters (ADCs) result in increased power consumption and expensive hardware costs. In this paper, we explore blind spectrum sensing utilizing one-bit ADCs. We derive a closed-form detector based on Rao's test and demonstrate its equivalence with the second-order eigenvalue-moment-ratio test. Furthermore, a near-exact distribution based on the moment-based method, and an approximate distribution in the low signal-to...
Source: IEEE Transactions on Signal Processing - January 4, 2024 Category: Biomedical Engineering Source Type: research

Wave Physics-Informed Matrix Factorizations
With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing techniques that incorporate known physical constraints into the learned representation. As one example, in many applications that involve a signal propagating through physical media (e.g., optics, acoustics, fluid dynamics, etc.), it is known that the dynamics of the signal must satisfy constraints imposed by the wave equation. Here we propose a matrix factorization technique that decomposes such signals into a sum of components, where each component is regularized t...
Source: IEEE Transactions on Signal Processing - January 3, 2024 Category: Biomedical Engineering Source Type: research

OPIT: A Simple but Effective Method for Sparse Subspace Tracking in High-Dimension and Low-Sample-Size Context
In recent years, sparse subspace tracking has attracted increasing attention in the signal processing community. In this paper, we propose a new provable effective method called OPIT (which stands for Online Power Iteration via Thresholding) for tracking the sparse principal subspace of data streams over time. Particularly, OPIT introduces a new adaptive variant of power iteration with space and computational complexity linear to the data dimension. In addition, a new column-based thresholding operator is developed to regularize the subspace sparsity. Utilizing both advantages of power iteration and thresholding operation,...
Source: IEEE Transactions on Signal Processing - January 3, 2024 Category: Biomedical Engineering Source Type: research

Collaborative-Prediction-Based Recursive Filtering for Nonlinear Systems Subject to Low-Duty-Cycle Scheduling
The objective of this study is to design a filtering scheme that can ensure the filtering performance for the nonlinear systems under the LDCS. To solve the problem of filtering performance degradation due to high data sparsity caused by the low duty cycle, the CPA combined with the zero-order holder (ZOH) is introduced into the filtering scheme. The desired gain matrix is first computed recursively by minimizing the obtained filtering error covariance upper matrix. Next, the boundedness of the filtering error covariance is discussed. Finally, the developed filtering approach based on the CPA and ZOH under the low-duty-cyc...
Source: IEEE Transactions on Signal Processing - January 2, 2024 Category: Biomedical Engineering Source Type: research

Social Opinion Formation and Decision Making Under Communication Trends
This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We study the case where agents share information about only one hypothesis, namely, the trending topic, which can be randomly changing at every iteration. We show that agents can learn the true hypothesis even if they do not discuss it, at rates comparable to traditional social learning. We also show that using one's own belief as a prior for estimating the neighbors’ non-tran...
Source: IEEE Transactions on Signal Processing - January 1, 2024 Category: Biomedical Engineering Source Type: research

Solving Linear Inverse Problems Using Higher-Order Annealed Langevin Diffusion
We propose a solution for linear inverse problems based on higher-order Langevin diffusion. More precisely, we propose pre-conditioned second-order and third-order Langevin dynamics that provably sample from the posterior distribution of our unknown variables of interest while being computationally more efficient than their first-order counterpart and the non-conditioned versions of both dynamics. Moreover, we prove that both pre-conditioned dynamics are well-defined and have the same unique invariant distributions as the non-conditioned cases. We also incorporate an annealing procedure that has the double benefit of furth...
Source: IEEE Transactions on Signal Processing - January 1, 2024 Category: Biomedical Engineering Source Type: research

Quickest Detection for Human-Sensor Systems Using Quantum Decision Theory
A sensor observes an underlying state of nature in noise, computes a posterior probability of this state, and provides this posterior as a recommendation to a human decision-maker. The human then makes decisions based on this recommendation. By recording these human decisions over time, how can a quickest detector detect a change in the underlying state? This paper addresses the above human-sensor interface problem. This framework generalizes classical quickest detection since the detector only has access to human decisions rather than noisy signals with known statistical properties. We utilize a Quantum Decision Theory mo...
Source: IEEE Transactions on Signal Processing - January 1, 2024 Category: Biomedical Engineering Source Type: research

Edgewise Outliers of Network Indexed Signals
We consider models for network-indexed multivariate data, also known as graph signals, involving a dependence between variables as well as across graph nodes. The dependence across nodes is typically established through the entries of the Laplacian matrix by imposing a distribution that relates the graph signal from one node to the next. Based on such distributional assumptions of the graph signal, we focus on outliers detection and introduce the new concept of edgewise outliers. For this purpose, we first derive the distribution of some sums of squares, in particular squared Mahalanobis distances that can be used to fix d...
Source: IEEE Transactions on Signal Processing - December 28, 2023 Category: Biomedical Engineering Source Type: research

Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design
Electronically tunable metasurfaces, or Intelligent Reflecting Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflecting elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel sensing/estimation, we propose a novel, fully data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for general two-stage (i.e., short/long-term), fully-passive IRS-aided stochastic utility maximization. ZoSGA learns long-term optimal IRS beamformers joint...
Source: IEEE Transactions on Signal Processing - December 25, 2023 Category: Biomedical Engineering Source Type: research

A One-Shot Framework for Distributed Clustered Learning in Heterogeneous Environments
The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments in which users obtain data from one of $K$ different distributions. In the proposed setup, the grouping of users (based on the data distributions they sample), as well as the underlying statistical properties of the distributions, are apriori unknown. A family of One-shot Distributed Clustered Learning methods (ODCL-$\mathcal{C}$) is proposed, parametrized by the set of admissible clustering algorithms $\mathcal{C}$, with the objective of learning the true model at each user. The admissible clustering metho...
Source: IEEE Transactions on Signal Processing - December 25, 2023 Category: Biomedical Engineering Source Type: research

Mixed Max-and-Min Fractional Programming for Wireless Networks
Fractional programming (FP) plays a crucial role in wireless network design because many relevant problems involve maximizing or minimizing ratio terms. Notice that the maximization case and the minimization case of FP cannot be converted to each other in general, so they have to be dealt with separately in most of the previous studies. Thus, an existing FP method for maximizing ratios typically does not work for the minimization case, and vice versa. However, the FP objective can be mixed max-and-min, e.g., one may wish to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate receiver while minimiz...
Source: IEEE Transactions on Signal Processing - December 25, 2023 Category: Biomedical Engineering Source Type: research

Latent-KalmanNet: Learned Kalman Filtering for Tracking From High-Dimensional Signals
The Kalman filter (KF) is a widely used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe an SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, we study tracking from high-d...
Source: IEEE Transactions on Signal Processing - December 22, 2023 Category: Biomedical Engineering Source Type: research

Main-Lobe Beamwidth Constrained Target Localization Using Bistatic Range Measurements
This study introduces an angular constraint to incorporate the beamwidth information into the elliptic localization, resulting in a Quadratically Constrained Quadratic Programming (QCQP) problem. A novel Two-Stage Main-lobe beamwidth Constrained Estimation (TSMCE) algorithm is proposed to solve this challenging localization problem. Theoretical analysis demonstrates that the algorithm can converge to the candidate solutions that satisfy the Karush–Kuhn–Tucker (KKT) optimality conditions. The corresponding covariance can achieve the Cramér-Rao Lower Bound (CRLB) under a small noise assumption. Numerical simulations con...
Source: IEEE Transactions on Signal Processing - December 22, 2023 Category: Biomedical Engineering Source Type: research

A Log-Linear Nonparametric Online Changepoint Detection Algorithm Based on Functional Pruning
Online changepoint detection aims to detect anomalies and changes in real time within high frequency data streams, sometimes with limited available computational resources. This is an important task that is rooted in many real-world applications including, but not limited to, cybersecurity, medicine and astrophysics. While fast and efficient online algorithms have been recently introduced, these rely on parametric assumptions which are often violated in practical applications. Motivated by data streams from the telecommunications sector, we build a flexible nonparametric approach to detect a change in the distribution of a...
Source: IEEE Transactions on Signal Processing - December 19, 2023 Category: Biomedical Engineering Source Type: research