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.

Channel Estimation for Quantized Systems Based on Conditionally Gaussian Latent Models
This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative models, specifically Gaussian mixture models (GMMs), mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These models effectively learn the unknown channel distribution inherent in radio propagation scenarios, providing valuable prior information. Conditioning on the latent variable of these generative models yields a locally Gaussian channel distribution, thus enabling the application of the well-known Bussgang decomposition....
Source: IEEE Transactions on Signal Processing - February 29, 2024 Category: Biomedical Engineering Source Type: research

Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains
We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of $(x_{n})$ on $(y_{n})$, causally conditioned on a possibly confounding third time series $(z_{n})$. Under natural Markovian modeling assumptions, it is shown that the null hypothesis, corresponding to the absence of temporally causal influence, is equivalent to the underlying ‘causal conditional directed information rate’ being equal to zero. The plug-in estimator for this functional is identified with the log...
Source: IEEE Transactions on Signal Processing - February 29, 2024 Category: Biomedical Engineering Source Type: research

RIS-Aided Radar for Target Detection: Clutter Region Analysis and Joint Active-Passive Design
Reconfigurable Intelligent Surface (RIS) brings a transformative potential to radar systems by providing a new dimension to control electromagnetic waves. This paper focuses on enhancing target detectability through the design and analysis of RIS-aided radar. A comprehensive signal model is established, recognizing both Line of Sight (LoS) and Non-Line of Sight (N-LoS) returns, factoring in RIS location and range gate shifts. This modeling enables an examination of the RIS-dependent effect of enlarged clutter region, underscoring the essential need for precise RIS phase optimization. A joint design problem encompassing tra...
Source: IEEE Transactions on Signal Processing - February 28, 2024 Category: Biomedical Engineering Source Type: research

Hybrid RIS-Assisted MIMO Dual-Function Radar-Communication System
Dual-function radar-communication (DFRC) technology is emerging in next-generation wireless systems. Reconfigurable intelligent surface (RIS) arrays have been suggested as a crucial sensor component of the DFRC. In this paper, we propose a hybrid RIS (HRIS)-assisted multiple-input multiple-output (MIMO) DFRC system, where the HRIS is capable of reflecting communication signals to mobile users and receiving the scattering signal reflected from the radar target simultaneously. Under such a scenario, we are interested in characterizing the fundamental trade-off between radar sensing and communication. Specifically, we study t...
Source: IEEE Transactions on Signal Processing - February 28, 2024 Category: Biomedical Engineering Source Type: research

Strong Convexity of Affine Phase Retrieval
Affine phase retrieval refers to the process of recovering a signal from intensity measurements, with some entries known in advance. In this paper, we demonstrate that a natural least squares formulation for affine phase retrieval is strongly convex on the complete space under certain mild conditions, given that the measurement vectors are complex Gaussian random vectors and that the number of measurements is $m\geq O(d\log d)$, where $d$ is the dimension of the signals. Based on the result, we prove that the simple gradient descent method for the affine phase retrieval converges linearly to the target solution with high p...
Source: IEEE Transactions on Signal Processing - February 28, 2024 Category: Biomedical Engineering Source Type: research

BCH Based U-UV Codes and Its SCL Decoding
U-UV codes are constructed by a number of component codes in the (U$ | $U$ + $V) recursive structure, where the U codes and V codes are component codes. This construction is known as the Plotkin construction and the U-UV codes are also known as the generalized concatenated codes with inner polar codes. This paper proposes U-UV codes with primitive BCH component codes as a pursuit of designing competent short-to-medium length codes for future ultra low-latency communications. The U-UV code design considers both the finite length rate of the subchannels and the equal error probability rule, yielding a good performing U-UV co...
Source: IEEE Transactions on Signal Processing - February 28, 2024 Category: Biomedical Engineering Source Type: research

From Global Statistic to Local Statistic: Micro-Doppler Period Estimation Based on Short-Time Similarity Statistic
Micro-Doppler (m-D) period is an important feature of micro-motion targets, playing a vital role in detecting and recognizing moving targets like ground vehicles, aircraft, and space debris. Existing m-D period estimation algorithms mostly exploit the periodicity of the global radar echo signals, and extra compensation steps are needed under high-order translations. To solve that problem, this work focuses on the periodicity of the local structure of radar signals, and a short-time similarity statistic framework is introduced. It is a standard m-D period estimation procedure where various statistics can be flexibly incorpo...
Source: IEEE Transactions on Signal Processing - February 23, 2024 Category: Biomedical Engineering Source Type: research

Fast and Accurate Output Error Estimation for Memristor-Based Deep Neural Networks
Memristors allow computing in memory, which may be leveraged by deep neural network (DNN) accelerators to reduce energy footprint. However, such gains in energy efficiency come at the cost of noise on the computation results due to the analog nature of memristors. In this work, we introduce a theoretical framework to estimate the mean squared error (MSE) of a memristor-based DNN. We propose an efficient software implementation of this framework which is shown to be orders of magnitude faster than using Monte-Carlo simulations. Additionally, we study two different techniques for mapping convolutional layers to memristors an...
Source: IEEE Transactions on Signal Processing - February 23, 2024 Category: Biomedical Engineering Source Type: research

Asymptotics of Distances Between Sample Covariance Matrices
This work considers the asymptotic behavior of the distance between two sample covariance matrices (SCM). A general result is provided for a class of functionals that can be expressed as sums of traces of functions that are separately applied to each covariance matrix. In particular, this class includes very conventional metrics, such as the Euclidean distance or Jeffrery's divergence, as well as a number of other more sophisticated distances recently derived from Riemannian geometry considerations, such as the log-Euclidean metric. In particular, we analyze the asymptotic behavior of this class of functionals by establish...
Source: IEEE Transactions on Signal Processing - February 22, 2024 Category: Biomedical Engineering Source Type: research

Overcoming Beam Squint in mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
The beam squint effect, which manifests in different steering matrices in different sub-bands, has been widely considered a challenge in millimeter wave (mmWave) multi-input multi-output (MIMO) channel estimation. Existing methods either require specific forms of the precoding/combining matrix, which restrict their general practicality, or simply ignore the beam squint effect by only making use of a single sub-band for channel estimation. Recognizing that different steering matrices are coupled by the same set of unknown channel parameters, this paper proposes to exploit the common sparsity structure of the virtual channel...
Source: IEEE Transactions on Signal Processing - February 22, 2024 Category: Biomedical Engineering Source Type: research

Decentralized Resource Allocation for Multi-Radar Systems Based on Quality of Service Framework
Resource allocation plays a crucial role in the design of multi-radar systems (MRS) for sensing applications. Conventional approaches involve centrally computing the resource allocation solution, assuming the existence of a fusion center (FC). However, these approaches lead to a significant computational burden associated with the FC and fail to yield a viable solution when employing decentralized network architectures. To address the limitations of the centralized approach, this paper proposes a decentralized resource allocation framework. The general resource allocation problem for MRS is comprehensively formulated as an...
Source: IEEE Transactions on Signal Processing - February 19, 2024 Category: Biomedical Engineering Source Type: research

A New Inexact Proximal Linear Algorithm With Adaptive Stopping Criteria for Robust Phase Retrieval
This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions are two adaptive stopping criteria for the subproblem. The convergence behavior of the proposed methods is analyzed. Through experiments on both synthetic and real datasets, we demonstrate that our methods are much more efficient than existing methods, such as the original proximal linear algorithm and the subgradient method. (Source: IEEE Transactions on Signal Processing)
Source: IEEE Transactions on Signal Processing - February 19, 2024 Category: Biomedical Engineering Source Type: research

FDA-MIMO Transmitter and Receiver Optimization
This paper addresses the joint design of the transmit parameters (i.e., radar code/frequency increments) and the receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system. The operating environment includes clutter, namely signal-dependent interference tied up to the FDA transmitted waveforms and the antenna array features, along with conventional thermal noise. The chosen optimization policy relies on the constrained maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) which for Gaussian interference is tantamount to maximizing the radar detection performance. In th...
Source: IEEE Transactions on Signal Processing - February 16, 2024 Category: Biomedical Engineering Source Type: research

Optimized Gradient Tracking for Decentralized Online Learning
This work considers the problem of decentralized online learning, where the goal is to track the optimum of the sum of time-varying functions, distributed across several nodes in a network. The local availability of the functions and their gradients necessitates coordination and consensus among the nodes. We put forth the Generalized Gradient Tracking (GGT) framework that unifies a number of existing approaches, including the state-of-the-art ones. The performance of the proposed GGT algorithm is theoretically analyzed using a novel semidefinite programming-based analysis that yields the desired regret bounds under very ge...
Source: IEEE Transactions on Signal Processing - February 15, 2024 Category: Biomedical Engineering Source Type: research

Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks
Optimizing large-scale wireless networks, including optimal resource management, power allocation, and throughput maximization, is inherently challenging due to their non-observable system dynamics and heterogeneous and complex nature. Herein, a novel ensemble $Q$-learning algorithm that addresses the performance and complexity challenges of the traditional $Q$-learning algorithm for optimizing wireless networks is presented. Ensemble learning with synthetic Markov Decision Processes is tailored to wireless networks via new models for approximating large state-space observable wireless networks. In particular, digital cous...
Source: IEEE Transactions on Signal Processing - February 15, 2024 Category: Biomedical Engineering Source Type: research