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.A Curriculum Learning Approach to Optimization with Application to Downlink Beamforming
We investigate neural networks’ ability to approximate the solution map of certain classes of beamforming optimization problems. The model is trained in an unsupervised manner to map a given channel realization to a near-optimal point of the corresponding optimization problem instance. Training is offline so that online optimization requires only the feedforward computation, the complexity of which is orders of magnitude less than state-of-the-art optimization algorithms. In order to obtain a near-optimal channel-beamformer mapping, either of two curriculum learning strategies is required: The reward curriculum employs a...
Source: IEEE Transactions on Signal Processing - December 7, 2023 Category: Biomedical Engineering Source Type: research
Coordinating Multiple Intelligent Reflecting Surfaces Without Channel Information
Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI. Blind beamforming only requires measuring the received signal power at the user terminal for a sequence of randomly generated phase shifts across all IRSs. The main idea is to extract the key st...
Source: IEEE Transactions on Signal Processing - December 7, 2023 Category: Biomedical Engineering Source Type: research
Minimizing Low-Rank Models of High-Order Tensors: Hardness, Span, Tight Relaxation, and Applications
We consider the problem of finding the smallest or largest entry of a tensor of order $N$ that is specified via its rank decomposition. Stated in a different way, we are given $N$ sets of $R$-dimensional vectors and we wish to select one vector from each set such that the sum of the Hadamard product of the selected vectors is minimized or maximized. We show that this fundamental tensor problem is NP-hard for any tensor rank higher than one, and polynomial-time solvable in the rank-one case. We also propose a continuous relaxation and prove that it is tight for any rank. For low-enough ranks, the proposed continuous reformu...
Source: IEEE Transactions on Signal Processing - December 4, 2023 Category: Biomedical Engineering Source Type: research
Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks
This paper investigates distributed detection of sparse stochastic signals with quantized measurements under Byzantine attacks, where sensors may send falsified data to the Fusion Center (FC) to degrade system performance. Here, the Bernoulli-Gaussian (BG) distribution is used to model sparse stochastic signals. Several detectors with significantly improved detection performance are proposed by incorporating estimates of attack parameters into the detection process. In the case of unknown sparsity degree and attack parameters, we propose the generalized likelihood ratio test with reference sensors (GLRTRS) as well as the l...
Source: IEEE Transactions on Signal Processing - December 1, 2023 Category: Biomedical Engineering Source Type: research
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e.g., frequency and space) radio power propagation maps from limited sensor measurements. While early methods often lacked theoretical support, recent works have demonstrated that radio maps can be provably recovered using low-dimensional models—such as the block-term tensor decomposition (BTD) model and certain deep generative models (DGMs)—of the high-dimensional multi-domain radio signals. However, these existing provable SC approaches assume that sensors send real-valued (full-resolution) measurements to the fusion c...
Source: IEEE Transactions on Signal Processing - November 29, 2023 Category: Biomedical Engineering Source Type: research
DINE: Decentralized Inexact Newton With Exact Linear Convergence Rate
Decentralized learning has recently attracted much research attention because of its robustness and user privacy advantages. Decentralized algorithms play central roles in training machine learning models in decentralized learning. Due to the slow convergence of first-order algorithms (e.g., SGD), several works try to exploit the second-order information of the local functions to obtain faster convergence rates and achieve the communication efficiency of decentralized learning. However, existing decentralized second-order algorithms, such as Network_DANE and Newton_tracking, can not achieve both computation and communicati...
Source: IEEE Transactions on Signal Processing - November 28, 2023 Category: Biomedical Engineering Source Type: research
Alternating Minimization for Wideband Multiuser IRS-Aided MIMO Systems Under Imperfect CSI
This work focuses on wideband intelligent reflecting surface (IRS)-aided multiuser MIMO systems. One of the major challenges of this scenario is the joint design of the frequency-dependent base station (BS) precoder and user filters, and the IRS phase-shift matrix which is frequency flat and common to all the users. In addition, we consider that the channel state information (CSI) is imperfect at both the transmitter and the receivers. A statistical model for the imperfect CSI is developed and exploited for the system design. A minimum mean square error (MMSE) approach is followed to determine the IRS phase-shift matrix, t...
Source: IEEE Transactions on Signal Processing - November 28, 2023 Category: Biomedical Engineering Source Type: research
Improving Angle Estimation via Feedback for Hybrid mmWave Systems Using Beamforming Virtual Arrays
It is known that when there is some rough knowledge on the angles of arrivals (AoAs) and angles of departures (AoDs), beamforming can increase the received SNR for finer angle estimation. Beamforming aided angle estimation generally requires two stages or more. Estimation performance depends on the allocation of training time and feedback bits between the two stages. In this paper, we consider the allocation of training time and feedback bits of a two-stage angle estimation scheme for a hybrid mmWave system that has limited feedback rate. Banded Toeplitz matrices are used for precoding and combining to obtain beamforming v...
Source: IEEE Transactions on Signal Processing - November 22, 2023 Category: Biomedical Engineering Source Type: research
Reward Teaching for Federated Multiarmed Bandits
Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In reality, however, it may not be possible to modify the clients’ existing protocols. To address this challenge, this work focuses on clients who always maximize their individual cumulative rewards, and introduces a novel idea of “reward teaching”, where the server guides the clients towards global optimality through implicit local reward adjustments. Under this framework, the server faces two tightly coupled tasks of bandit learning and targe...
Source: IEEE Transactions on Signal Processing - November 22, 2023 Category: Biomedical Engineering Source Type: research
An Improved GraDe Method for Blind Separation of Graph Signals
For blind source separation (BSS) of Gaussian graph signals, an algorithm called GraDe (graph decorrelation) has been previously introduced. In the current paper, it is shown that GraDe does not achieve a good performance for some types of graphs. This is attributed to the estimation of covariance/autocovariance matrices using signal samples, which may not be reliable. To address this weakness, an improvement based on the spectral representation of the signals is proposed, focusing on removing the impact of the outlier eigenvalues. Numerical simulations show that the proposed method outperforms the original GraDe algorithm...
Source: IEEE Transactions on Signal Processing - November 22, 2023 Category: Biomedical Engineering Source Type: research
Recovery Guarantee Analyses of Joint Sparse Recovery via Tail $ \ell _{2,1}$ Minimization
Recovery guarantee analyses of the tail-$ \ell _{2,1}$ minimization approach applied to multiple measurement vector (MMV) model are presented. Exact joint sparse recovery from the MMV model benefits from the rank enrichment of $rank(\boldsymbol{X})$. Generally speaking, unique sparse solution exists for sparsity level $k (Source: IEEE Transactions on Signal Processing)
Source: IEEE Transactions on Signal Processing - November 22, 2023 Category: Biomedical Engineering Source Type: research
Ellipsoid Fitting With the Cayley Transform
We introduce Cayley transform ellipsoid fitting (CTEF), an algorithm that uses the Cayley transform to fit ellipsoids to noisy data in any dimension. Unlike many ellipsoid fitting methods, CTEF is ellipsoid specific, meaning it always returns elliptic solutions, and can fit arbitrary ellipsoids. It also significantly outperforms other fitting methods when data are not uniformly distributed over the surface of an ellipsoid. Inspired by growing calls for interpretable and reproducible methods in machine learning, we apply CTEF to dimension reduction, data visualization, and clustering in the context of cell cycle and circadi...
Source: IEEE Transactions on Signal Processing - November 21, 2023 Category: Biomedical Engineering Source Type: research
Revisiting Deep Generalized Canonical Correlation Analysis
Canonical correlation analysis (CCA) is a classic statistical method for discovering latent co-variation that underpins two or more observed random vectors. Several extensions and variations of CCA have been proposed that have strengthened our capabilities in terms of revealing common random factors from multiview datasets. In this work, we first revisit the most recent deterministic extensions of deep CCA and highlight the strengths and limitations of these state-of-the-art methods. Some methods allow trivial solutions, while others can miss weak common factors. Others overload the problem by also seeking to reveal what i...
Source: IEEE Transactions on Signal Processing - November 21, 2023 Category: Biomedical Engineering Source Type: research
Clustered Federated Learning via Generalized Total Variation Minimization
We study optimization methods to train local (or personalized) models for decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-specific notions of similarity between local datasets. Examples of such notions include spatio-temporal proximity, statistical dependencies or functional relations. Our main conceptual contribution is to formulate federated learning as generalized total variation (GTV) minimization. This formulation unifies and considerably extends existing federated learning methods. It is highly flexible and can be combined with a broad range o...
Source: IEEE Transactions on Signal Processing - November 21, 2023 Category: Biomedical Engineering Source Type: research
FedFM: Anchor-Based Feature Matching for Data Heterogeneity in Federated Learning
One of the key challenges in federated learning (FL) is local data distribution heterogeneity across clients, which may cause inconsistent feature spaces across clients. To address this issue, we propose Federated Feature Matching (FedFM), which guides each client's features to match shared category-wise anchors (landmarks in feature space). This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space. We tackle the challenge of varying objective functions in theoretical analysis and provide convergence guarantee for FedFM. In FedFM, to mitigate the phenomenon of...
Source: IEEE Transactions on Signal Processing - November 21, 2023 Category: Biomedical Engineering Source Type: research
