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Tracking Control for Stochastic Learning Systems over Changing Durations IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-15 Wenjin Lv, Deyuan Meng, Jingyao Zhang, Kaiquan Cai
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Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-15 Ly V. Nguyen, A. Lee Swindlehurst, Duy H. N. Nguyen
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1-Bit Tensor Completion via Max-and-Nuclear-Norm Composite Optimization IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-12 Wenfei Cao, Xia Chen, Shoucheng Yan, Zeyue Zhou, Andrzej Cichocki
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Random ISAC Signals Deserve Dedicated Precoding IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-12 Shihang Lu, Fan Liu, Fuwang Dong, Yifeng Xiong, Jie Xu, Ya-Feng Liu, Shi Jin
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Multi-Channel Factor Analysis: Identifiability and Asymptotics IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-12 Gray Stanton, David Ramírez, Ignacio Santamaria, Louis Scharf, Haonan Wang
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Orthogonal Expanded Memory Polynomial Model for Circular Complex Gaussian Processes IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-12 Xiaojing Huang, Anh Tuyen Le, Hao Zhang, J. Andrew Zhang, Y. Jay Guo
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Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-11 Zhi-Yong Wang, Hing Cheung So, Abdelhak M. Zoubir
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Robust Gaussian Mixture Modeling: A K-Divergence Based Approach IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-11 Ori Kenig, Koby Todros, Tulay Adali
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Diffusion Moving-Average Adaptation Over Networks IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-11 Yishu Peng, Sheng Zhang, Zhengchun Zhou, Hongyang Chen, Ali H. Sayed
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Distributed Quasi-Newton Method for Multi-Agent Optimization IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-08 Ola Shorinwa, Mac Schwager
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Bayesian Hierarchical Sparse Autoencoder for Massive MIMO CSI Feedback IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-05 Huayan Guo, Vincent K. N. Lau
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Channel Estimation and Beamforming for Beyond Diagonal Reconfigurable Intelligent Surfaces IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-05 Hongyu Li, Shanpu Shen, Yumeng Zhang, Bruno Clerckx
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Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-05 Hoa Van Nguyen, Ba-Ngu Vo, Ba-Tuong Vo, Hamid Rezatofighi, Damith C. Ranasinghe
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Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-05 Yixiao Yang, Ran Tao, Kaixuan Wei, Jun Shi
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A Generalization of the Convolution Theorem and its Connections to Non-Stationarity and the Graph Frequency Domain IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-04 Alberto Natali, Geert Leus
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3-D Rigid Body Localization Using 1-D AOA: Boundary Condition Analysis and Generic Majorization-Minimization Framework IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-04 Pan Li, Jianfeng Li, Xiaofei Zhang, Qihui Wu
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Distributed adaptive Bernoulli filtering for multi-sensor target tracking under uncertainty IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-03 Lihong Shi, Giorgio Battistelli, Luigi Chisci, Feng Yang, Litao Zheng
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Asymptotically Tight Bayesian Cramér-Rao Bound IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-03 Ori Aharon, Joseph Tabrikian
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Parameter Estimation of a Single Chirp in the Presence of Wiener Phase Noise With Unknown Variance IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-02 Mingkai Ding, Yinsheng Wei, Lei Yu
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Do algorithms and barriers for sparse principal component analysis extend to other structured settings? IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-02 Guanyi Wang, Mengqi Lou, Ashwin Pananjady
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Bispectrum Unbiasing for Dilation-Invariant Multi-reference Alignment IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-01 Liping Yin, Anna Little, Matthew Hirn
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A Unified Framework for STAR-RIS Coefficients Optimization IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-07-01 Hancheng Zhu, Yuanwei Liu, Yik-Chung Wu, Vincent K. N. Lau
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Direct Target Localization for Distributed Passive Radars With Direct-Path Interference Suppression IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-28 Qiyu Zhou, Ye Yuan, Luca Venturino, Wei Yi
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Intelligent Reflecting Surface-Aided Electromagnetic Stealth Against Radar Detection IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-27 Beixiong Zheng, Xue Xiong, Jie Tang, Rui Zhang
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Fast Recursive Greedy Methods for Sparse Signal Recovery IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-26 Min Xiang, Zhenyue Zhang
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Detecting Low Pass Graph Signals via Spectral Pattern: Sampling Complexity and Applications IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-26 Chenyue Zhang, Yiran He, Hoi-To Wai
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Pivotal Auto-Encoder via Self-Normalizing ReLU IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-26 Nelson Goldenstein, Jeremias Sulam, Yaniv Romano
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MSE-Based Training and Transmission Optimization for MIMO ISAC Systems IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-24 Zhenyao He, Hong Shen, Wei Xu, Yonina C. Eldar, Xiaohu You
In this paper, we investigate a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system under typical block-fading channels. As a non-trivial extension to most existing works on ISAC, both the training and transmission signals sent by the ISAC transmitter are exploited for sensing. Specifically, we develop two training and transmission design schemes to minimize a weighted
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EKFNet: Learning System Noise Covariance Parameters for Nonlinear Tracking IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-20 Liang Xu, Ruixin Niu
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The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-17 Junli Fang, João F. C. Mota, Baoshan Lu, Weicheng Zhang, Xuemin Hong
The joint source-channel coding (JSCC) framework leverages deep learning to learn from data the best codes for source and channel coding. When the output signal, rather than being binary, is directly mapped onto the IQ domain (complex-valued), we call the resulting framework joint source coding and modulation (JSCM). We consider a JSCM scenario and show the existence of a strict tradeoff between channel
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Marginalized Beam Search Algorithms for Hierarchical HMMs IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-17 Xuechun Xu, Joakim Jaldén
Inferring a state sequence from a sequence of measurements is a fundamental problem in bioinformatics and natural language processing. The Viterbi and the Beam Search (BS) algorithms are popular inference methods, but they have limitations when applied to Hierarchical Hidden Markov Models (HHMMs), when the primary interest lies in the outer state sequence. The Viterbi algorithm can not infer outer
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Online Bayesian Learning and Inference for OTHR Target Tracking and Registration IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-17 Hua Lan, Yuxiang Mao, Zengfu Wang
Coordinate registration (CR), using ionospheric information to map the measurement in radar slant coordinates into geodetic inertial coordinates, plays a crucial role in target tracking of over-the-horizon radar (OTHR). Due to the ionospheric inherent variability and inaccurate modeling, there exists uncertainty in the CR process, decreasing target tracking accuracy. By formulating the OTHR target
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Active Sensing for Reciprocal MIMO Channels IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-11 Tao Jiang, Wei Yu
This paper addresses the design of transmit precoder and receive combiner matrices to support $N_{\rm s}$ independent data streams over a time-division duplex (TDD) point-to-point massive multiple-input multiple-output (MIMO) channel with either a fully digital or a hybrid structure. The optimal precoder and combiner design amounts to finding the top- $N_{\rm s}$ singular vectors of the channel matrix
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Green Cell-Free Massive MIMO: An Optimization Embedded Deep Reinforcement Learning Approach IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-10 Guangchen Wang, Peng Cheng, Zhuo Chen, Branka Vucetic, Yonghui Li
Cell-free massive multiple-input multiple-output (MIMO) deploys a large number of distributed access points (APs) without cell edges, offering seamless connectivity with significantly increased spectral efficiency and system capacity. However, a dedicated fronthaul link is required to connect each AP to a central processing unit (CPU), and the transmissions and hardware-related static power supplies
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On Centralization and Unitization of Batch Normalization for Deep ReLU Neural Networks IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-06 Wen Fei, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Batch normalization (BN) enhances the training of deep ReLU neural network with a composition of mean centering (centralization) and variance scaling (unitization). Despite the success of BN, there lacks a theoretical explanation to elaborate the effects of BN on training dynamics and guide the design of normalization methods. In this paper, we elucidate the effects of centralization and unitization
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Theoretical Insights and Practical Algorithms for Transceiver Design of PMCW Radar IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-04 Ronghao Lin, Zhuoli Liu, Yutao Chen, Hing Cheung So, Jian Li
Phase-modulated continuous-wave (PMCW) radar can fundamentally solve the growing mutual interference problems suffered by the widely used linear frequency-modulated continuous-wave automotive radar. To achieve this objective, long binary periodic probing sequences, characterized by low auto-correlation sidelobes, arbitrary period lengths, and substantial diversity, should be transmitted by the PMCW
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Augmented Affine Projection Algorithm Based on Error Nonlinearity and Its Simplified Version IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-04 Yanglong Gu, Jingen Ni
The augmented affine projection algorithm (AAPA) has a faster convergence rate than the augmented complex-valued LMS (ACLMS) algorithm for correlated inputs. However, they are both not robust against impulsive interference. This work proposes an improved AAPA based on error nonlinearity, which is implemented with the derivative of the logistic distance metric. To reduce its computational complexity
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Hybrid-Field Channel Estimation for XL-MIMO Systems With Stochastic Gradient Pursuit Algorithm IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-03 Hao Lei, Jiayi Zhang, Zhe Wang, Bo Ai, Derrick Wing Kwan Ng
Extremely large-scale multiple-input multiple-output (XL-MIMO) is crucial for satisfying the high data rate requirements of the sixth-generation (6G) wireless networks. In this context, ensuring accurate acquisition of channel state information (CSI) with low complexity becomes imperative. Moreover, deploying an extremely large antenna array at the base station (BS) might result in some scatterers
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Physical Beam Sharing for Communications With Multiple Low Earth Orbit Satellites IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-06-03 Yan-Yin He, Shang-Ho Tsai, H. Vincent Poor
Physical (analog) beamforming is expected to become an important technique in Low Earth Orbit (LEO) satellite transmission in upcoming 6G communications. To build dense networks via LEO satellites and decrease deployment expenses, the corresponding satellites should have minimal hardware, low computational complexity, and limited power consumption. Additionally, issues such as different propagation
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The WQN Algorithm for EEG Artifact Removal in the Absence of Scale Invariance IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-31 Matteo Dora, Stéphane Jaffard, David Holcman
Electroencephalogram (EEG) signals reflect brain activity across different brain states, characterized by distinct frequency distributions. Through multifractal analysis tools, we investigate the scaling behaviour of several classes of EEG signals and artifacts. We show that brain states associated to sleep and general anaesthesia are not in general characterized by scale invariance. The lack of scale
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On Partial Smoothness, Activity Identification and Faster Algorithms of $L_{1}$ Over $L_{2}$ Minimization IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-30 Min Tao, Xiao-Ping Zhang, Zi-Hao Xia
The $L_{1}/L_{2}$ norm ratio arose as a sparseness measure and attracted a considerable amount of attention due to three merits: (i) sharper approximations of $L_{0}$ compared to the $L_{1}$ ; (ii) parameter-free and scale-invariant; (iii) more attractive than $L_{1}$ under highly-coherent matrices. In this paper, we first establish the partly smooth property of $L_{1}$ over $L_{2}$ minimization relative
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CRB Analysis for Mixed-ADC Based DOA Estimation IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-29 Xinnan Zhang, Yuanbo Cheng, Xiaolei Shang, Jun Liu
We consider a mixed analog-to-digital converter (ADC) based architecture consisting of high-precision and one-bit ADCs with the antenna-varying threshold for direction of arrival (DOA) estimation using a uniform linear array (ULA), which utilizes fixed but different thresholds for one-bit ADCs across different receive antennas. The Cramér-Rao bound (CRB) with the antenna-varying threshold is obtained
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Identifiable Solutions to Foreground Signature Extraction from Hyperspectral Images in an Intimate Mixing Scenario IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-29 Jarrod Hollis, Raviv Raich, Jinsub Kim, Barak Fishbain, Shai Kendler
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Extended Object Tracking Using Aspect Ratio IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-28 Le Zhang, Jian Lan
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Subband-split Co-Prime Spectral Analyzer with Convolution Window Prototype Filters IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-27 Yuyan Wei, Xiangdong Huang, Jinshui Song, Yanping Li
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Channel Estimation for Hybrid mmWave Systems Using Generalized Kronecker Compressive Sensing (G-KCS) With Successive Decision-Aided Recovery IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-27 Yu-Tai Chiew, Yuan-Pei Lin
It is known that compressive sensing (CS) techniques are useful for the estimation of millimeter wave (mmWave) channels. When uniform planar arrays (UPA) are used, four dictionaries, two for angles of departure (AoD) and two for angles of arrival (AoA), are constructed and mmWave channel estimation becomes a four-dimensional CS problem. The sensing matrix in the CS formulation, containing the Kronecker
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Optimization of High-resolution and Ambiguity-free Sparse Planar Array Geometry for Automotive MIMO Radar IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-24 Mingsai Huan, Junli Liang, Yugang Ma, Wei Liu, Yifan Wu, Yonghong Zeng
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Robust Target Localization in 2D: A Value-at-Risk Approach IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-24 João Domingos, João Xavier
This paper considers the problem of locating a target in 2D from range measurements containing outliers. Assuming the number of outliers is known, we formulate the problem of minimizing inlier losses while ignoring outliers. This leads to a combinatorial, non-convex, non-smooth problem involving the percentile function. Using the framework of risk analysis from Rockafellar et al., we start by interpreting
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$\ell_{1}-\ell_{q}$ ($1 IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-24 Yancheng Lu, Ning Bi, Anhua Wan
One-bit compressed sensing has found broad applications. Due to the constraint on the unit sphere, the classic $\ell_{1}$ minimization frequently returns a signal which is not sparse enough. In this paper, $\ell_{1}-\ell_{q} (1
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Transmit Hardware Impairment Aware Waveform Design for MIMO DFRC IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-22 Baoxi Guo, Junli Liang, Tao Wang, Hing Cheung So, Jin Xu
In this paper, we address the problem of waveform design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system in the presence of transmit hardware impairments. Under this scenario, the actual transmit waveform is a distorted version of the expected waveform, and thus may lower system performance. To achieve robustness against distortion in the waveform design
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Integrated MIMO Passive Radar Target Detection IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-20 Amir Zaimbashi, Maria Sabrina Greco, Fulvio Gini
Integrated passive radar (IPR) can be regarded as next-generation passive radar technology, which aims to integrate communication and radar systems. Unlike conventional passive radar, which does not prioritize communication-centric radar technology, IPR technology places a higher priority on incorporating specific radar constraints to develop waveforms that are better suited for radar applications
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Stable Estimation of Pulses of Unknown Shape From Multiple Snapshots via ESPRIT IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-20 Meghna Kalra, Kiryung Lee
We consider the problem of resolving overlapping pulses from noisy multi-snapshot measurements, which has been a problem central to various applications including medical imaging and array signal processing. ESPRIT algorithm has been used to estimate the pulse locations. However, existing theoretical analysis is restricted to ideal assumptions on signal and measurement models. We present a novel perturbation
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Localization by Doppler Derivatives and Doppler-Shifted Frequencies IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-17 Xiaochuan Ke, K. C. Ho
When a narrow-band source travels over a receiver, the observed signal will experience a Doppler frequency shift that can be exploited to determine its location. The Doppler shift is not constant even if the motion is linear with constant velocity and it has a rate of change. This work explores the Doppler derivatives (DDs) in addition to the Doppler-shifted frequencies (DSFs) encountered by a number
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On the Sum of Products of Independent Complex Normal Variables: Understanding the Fundamental SNR Gain Limit of MIMO-RIS IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-17 Fabien Héliot, Rahim Tafazolli
The dual objective of this paper is to first characterize the probability distribution of the random variable (RV), $U$ , equivalent to the sum of multiple products of two independent circularly symmetric complex normal (CN) RVs, in order to then evaluate the fundamental limit of multi-input multi-output (MIMO)-reconfigurable intelligent surface (RIS) communication systems in terms of channel/signal-to-noise
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Learning Multi-Frequency Partial Correlation Graphs IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-16 Gabriele D’Acunto, Paolo Di Lorenzo, Francesco Bonchi, Stefania Sardellitti, Sergio Barbarossa
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands. Motivated by many applications in which this differentiation is pivotal, we overcome this limitation by learning a block-sparse, frequency-dependent, partial
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Social Learning in Community Structured Graphs IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-15 Valentina Shumovskaia, Mert Kayaalp, Ali H. Sayed
Traditional social learning frameworks consider environments with a homogeneous state, where each agent receives observations conditioned on that true state of nature. In this work, we relax this assumption and study the distributed hypothesis testing problem in a heterogeneous environment, where each agent can receive observations conditioned on their own personalized state of nature (or truth). We
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Sequential Joint Detection and Estimation: Optimal Average Stopping Time IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-15 Jilong Lyu, Enbin Song, Zhi Li, Jinming Liu, Yiguang Liu, Juping Gu, Qingjiang Shi
This paper considers a binary sequential joint detection and estimation problem, which aims to find an optimal scheme to minimize the average stopping time under the constraints on the detection error probabilities and the estimation errors. The scheme consists of a randomized stopping rule, a randomized detector and two estimators under two different decisions. To obtain the optimal scheme, we need
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Event-Triggered Proximal Online Gradient Descent Algorithm for Parameter Estimation IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-13 Yaoyao Zhou, Gang Chen
The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mechanism is introduced and the novel event-triggered proximal online gradient descent algorithm (EPOGDA)
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Velocity Profiling of a Distributed Target in Fluctuating SNR Environments IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-13 Akankshya Bhatta, Sibi Raj B. Pillai, Tummalapalli Venkata Chandrasekhar Sarma, Satish Mulleti
We consider the problem of tracking the velocity profile of a target or phenomenon, which is distributed across its range, such as atmospheric wind velocity across altitudes from radar measurements. Typical pulsed radars apply spectral techniques on the matched filtered samples collected from the return echoes at each range bin. Both the true signal as well as the false targets can lead to prominent
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On Optimal Tracking of Rapidly Varying Telecommunication Channels IEEE Trans. Signal Process. (IF 4.6) Pub Date : 2024-05-13 Maciej Niedźwiecki, Artur Gańcza
When parameters of mobile telecommunication channels change rapidly, classical adaptive filters, such as exponentially weighted least squares algorithms or gradient algorithms, fail to estimate them with sufficient accuracy. In cases like this, one can use identification methods based on explicit models of parameter changes such as the method of basis functions (BF). When prior knowledge about parameter