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Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-14 Luca Barbieri, Bernardo Camajori Tedeschini, Mattia Brambilla, Monica Nicoli
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Percentile Optimization in Wireless Networks—Part I: Power Control for Max-Min-Rate to Sum-Rate Maximization (and Everything in Between) IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-14 Ahmad Ali Khan, Raviraj S. Adve
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Joint Design of Binary Probing Sequence Sets and Receive Filter Banks for MIMO PMCW Radar IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-14 Yutao Chen, Yuanbo Cheng, Ronghao Lin, Hing Cheung So, Jian Li
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Learning Spatio-Temporal Graphical Models From Incomplete Observations IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-13 Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
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UL-DL Duality for Cell-free Massive MIMO with Per-AP Power and Information Constraints IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-13 Lorenzo Miretti, Renato L. G. Cavalcante, Emil Björnson, Sławomir Stańczak
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Message Passing based Wireless Multipath SLAM with Continuous Measurements Correction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-12 Jiawei Gao, Jiancun Fan, Shiyu Zhai, Gang Dai
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Augmented Multi-Subarray Dilated Nested Array with Enhanced Degrees of Freedom and Reduced Mutual Coupling IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-12 Hua Chen, Hongguang Lin, Wei Liu, Qing Wang, Qing Shen, Gang Wang
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Geometrically-Regularized Fast Independent Vector Extraction by Pure Majorization-Minimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Rintaro Ikeshita, Tomohiro Nakatani
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On a Novel Time-Varying Up-sampling Rate (TVUSR) Structure and its Statistical Properties IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Ayan Kumar Dutta, Shiv Dutt Joshi, Brejesh Lall
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A tensor based varying-coefficient model for multi-modal neuroimaging data analysis IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Pratim Guha Niyogi, Martin A. Lindquist, Tapabrata Maiti
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Scattering and Gathering for Spatially Varying Blurs IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Nicholas Chimitt, Xingguang Zhang, Yiheng Chi, Stanley H. Chan
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Chaotic Convergence of Newton’s method IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Jont B Allen
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Decentralized Stochastic Optimization with Pairwise Constraints and Variance Reduction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-08 Fei Han, Xuanyu Cao, Yi Gong
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Hybrid Data-Induced Kalman Filtering Approach and Application in Beam Prediction and Tracking IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-08 Jianjun Zhang, Yongming Huang, Christos Masouros, Xiaohu You, Björn Ottersten
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Multisensor Multiobject Tracking with Improved Sampling Efficiency IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-07 Wenyu Zhang, Florian Meyer
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mPage: Probabilistic Gradient Estimator with Momentum for Non-convex Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-06 Yuqing Liang, Hui Su, Jinlan Liu, Dongpo Xu
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Bayesian Inference for Non-linear forward model by using a VAE-based neural network structure IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-06 Yechuan Zhang, Jian-Qing Zheng, Michael Chappell
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Percentile Optimization in Wireless Networks—Part II: Beamforming for Cell-Edge Throughput Maximization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-04 Ahmad Ali Khan, Raviraj S. Adve
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Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-04 Talha Bozkus, Urbashi Mitra
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Optimal Transport Based Impulse Response Interpolation in the Presence of Calibration Errors IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-01 David Sundström, Filip Elvander, Andreas Jakobsson
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Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-29 Benedikt Fesl, Nurettin Turan, Benedikt Böck, Wolfgang Utschick
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Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-29 Andreas Theocharous, Georgia G. Gregoriou, Panagiotis Sapountzis, Ioannis Kontoyiannis
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
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RIS-Aided Radar for Target Detection: Clutter Region Analysis and Joint Active-Passive Design IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-28 Zhuang Xie, Linlong Wu, Jiahua Zhu, Marco Lops, Xiaotao Huang, Bhavani Shankar
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Strong Convexity of Affine Phase Retrieval IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-28 Meng Huang, Zhiqiang Xu
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
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BCH Based U-UV Codes and Its SCL Decoding IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-28 Wenhao Chen, Jinjun Cheng, Changyu Wu, Li Chen, Huazi Zhang
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
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Hybrid RIS-Assisted MIMO Dual-Function Radar-Communication System IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-28 Zhuoyang Liu, Haiyang Zhang, Tianyao Huang, Feng Xu, Yonina C. Eldar
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From Global Statistic to Local Statistic: Micro-Doppler Period Estimation Based on Short-Time Similarity Statistic IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-23 Yiwei Dai, Wenpeng Zhang, Yongxiang Liu
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
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Fast and Accurate Output Error Estimation for Memristor-Based Deep Neural Networks IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-23 Jonathan Kern, Sébastien Henwood, Gonçalo Mordido, Elsa Dupraz, Abdeldjalil Aïssa-El-Bey, Yvon Savaria, François Leduc-Primeau
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
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Asymptotics of Distances Between Sample Covariance Matrices IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-22 Roberto Pereira, Xavier Mestre, David Gregoratti
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Constructing Indoor Region-based Radio Map without Location Labels IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-22 Zheng Xing, Junting Chen
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Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-22 Bin Wang, Jun Fang, Hongbin Li, Yonina C. Eldar
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Overcoming Beam Squint in mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-22 Le Xu, Lei Cheng, Ngai Wong, Yik-Chung Wu, H. Vincent Poor
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
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Decentralized Resource Allocation for Multi-Radar Systems Based on Quality of Service Framework IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-19 Ye Yuan, Xinyu Liu, Wujun Li, Wei Yi, Wan Choi
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
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A New Inexact Proximal Linear Algorithm With Adaptive Stopping Criteria for Robust Phase Retrieval IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-19 Zhong Zheng, Shiqian Ma, Lingzhou Xue
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
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FDA-MIMO Transmitter and Receiver Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-16 Lan Lan, Massimo Rosamilia, Augusto Aubry, Antonio De Maio, Guisheng Liao
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Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-15 Talha Bozkus, Urbashi Mitra
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
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Optimized Gradient Tracking for Decentralized Online Learning IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-15 Shivangi Dubey Sharma, Ketan Rajawat
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Joint principal component analysis and supervised k means algorithm via non-iterative analytic optimization approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-14 Zhanbin Zhang, Bingo Wing-Kuen Ling, Guoheng Huang
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Maximin Design of Wideband Constant Modulus Waveform for Distributed Precision Jamming IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-14 Zhongping Yang, Zhihui Li, Xianxiang Yu, Qingsong Zhou, Chao Huang, Jianyun Zhang
Distributed precision jamming (DPJ) is an efficient way to control the combined power spectrum (CPS) of both target and friendly devices in electronic warfare. However, the existing methods neglect the design of worst-case CPS performance, and a great challenge is posed in determining an appropriate Pareto parameter to protect the friendly devices in practice. To address these issues, this paper investigates
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Lie Group Algebra Convolutional Filters IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-14 Harshat Kumar, Alejandro Parada-Mayorga, Alejandro Ribeiro
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Precoder Design for Massive MIMO Downlink With Matrix Manifold Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-12 Rui Sun, Chen Wang, An-An Lu, Xiqi Gao, Xiang-Gen Xia
We investigate the weighted sum-rate (WSR) maximization linear precoder design for massive multiple-input multiple-output (MIMO) downlink. We consider a single-cell system with multiple users and propose a unified matrix manifold optimization framework applicable to total power constraint (TPC), per-user power constraint (PUPC) and per-antenna power constraint (PAPC). We prove that the precoders under
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Communication-Efficient Design for Quantized Decentralized Federated Learning IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-08 Li Chen, Wei Liu, Yunfei Chen, Weidong Wang
Decentralized federated learning (DFL) is a variant of federated learning, where edge nodes only communicate with their one-hop neighbors to learn the optimal model. However, as information exchange is restricted in a range of one-hop in DFL, inefficient information exchange leads to more communication rounds to reach the targeted training loss. This greatly reduces the communication efficiency. In
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Distributed Continual Learning With CoCoA in High-Dimensional Linear Regression IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-05 Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
We consider estimation under scenarios where the signals of interest exhibit change of characteristics over time. In particular, we consider the continual learning problem where different tasks, e.g., data with different distributions, arrive sequentially and the aim is to perform well on the newly arrived task without performance degradation on the previously seen tasks. In contrast to the continual
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Probe: Learning Users’ Personal Projection Bias in Inter-Temporal Choices IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-05 Qingming Li, H. Vicky Zhao
Inter-temporal choices involve making decisions that require weighing costs in the present against benefits in the future. One specific type of inter-temporal choice is the decision between purchasing an individual item at full price or opting for a bundle including that item at a discounted price. Previous works assume that users have accurate expectations of factors involved in these decisions. However
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A New Approach for Graph Signal Separation Based on Smoothness IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-05 Mohammad-Hassan Ahmad Yarandi, Massoud Babaie-Zadeh
Blind source separation (BSS) is a signal processing subject that has recently been extended to graph signals. Graph signals that are smooth on their own graphs provide an opportunity to separate them from their summation by knowing their underlying graphs, which is different from the conventional BSS that requires at least two mixtures of source signals. In this paper, we introduce an approach to
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Corrections to “Semiparametric CRB and Slepian-Bangs Formulas for Complex Elliptically Symmetric Distributions” IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-31 Stefano Fortunati, Fulvio Gini, Maria S. Greco, Abdelhak M. Zoubir, Muralidhar Rangaswamy
Errors in [1] are corrected below. 1. In Eq. (17), $\mathrm{vecs}(\boldsymbol{\Sigma}_{0})$ should be $\mathrm{vec}(\boldsymbol{\Sigma}_{0})$. Specifically, the correct version of Eq. (17) is: \begin{align*} \mathbf{s}_{\boldsymbol{\phi}_{0}}\triangleq\nabla_{\boldsymbol{\phi}}\ln p_{Z}(\mathbf{z};\boldsymbol{\phi}_{0},h_{0})=[\mathbf{s}^{T}_{\boldsymbol{\mu}_{0}},\mathbf{s}^{T}_{\boldsymbol{\mu}^{*}_
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Instantaneous Frequency and Amplitude Estimation in Multicomponent Signals Using an EM-Based Algorithm IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-02 Quentin Legros, Dominique Fourer, Sylvain Meignen, Marcelo A. Colominas
This paper addresses the problem of estimating the instantaneous frequency (IF) and amplitude of the modes composing a non-stationary multicomponent signal in the presence of noise. A novel observation model for the signal spectrogram is developed within a Bayesian framework to handle intricate configurations involving noise or overlapping components. The model parameters are estimated using a stochastic
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Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-02 Maaz Mahadi, Tarig Ballal, Muhammad Moinuddin, Tareq Y. Al-Naffouri, Ubaid M. Al-Saggaf
Linear discriminant analysis (LDA) is a widely used technique for data classification. The method offers adequate performance in many classification problems, but it becomes inefficient when the data covariance matrix is ill-conditioned. This often occurs when the feature space's dimensionality is higher than or comparable to the training data size. Regularized LDA (RLDA) methods based on regularized
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Joint Design of Intra–Inter Agile Pulses and Doppler Filter Banks for Doppler Ambiguous Target IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-02 Tao Fan, Guolong Cui, Xianxiang Yu, Yi Bu, Lingjiang Kong, Xiaobo Yang
This paper deals with the joint design of intra-inter agile pulses and Doppler filter banks to improve Doppler ambiguous target detectability in signal-dependent mainlobe clutter. Assuming that the target's range and Doppler frequency (RDF) belong to an uncertainty set and that the clutter knowledge is partially known, a robust processing procedure based on a pulse compression matched filter and Doppler
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High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-01 Yuqing Ren, Yifei Shen, Leyu Zhang, Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, Emmanuel Boutillon, Andreas Burg, Chuan Zhang
Due to its high parallelism, belief propagation (BP) decoding is amenable to high-throughput applications and thus represents a promising solution for the ultra-high peak data rate required by future communication systems. To bridge the performance gap compared to the widely used successive cancellation list (SCL) decoding algorithm, BP list (BPL) decoding for polar codes extends candidate codeword
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Two New Algorithms for Maximum Likelihood Estimation of Sparse Covariance Matrices With Applications to Graphical Modeling IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-01 Ghania Fatima, Prabhu Babu, Petre Stoica
In this paper, we propose two new algorithms for maximum-likelihood estimation (MLE) of high dimensional sparse covariance matrices. Unlike most of the state-of-the-art methods, which either use regularization techniques or penalize the likelihood to impose sparsity, we solve the MLE problem based on an estimated covariance graph. More specifically, we propose a two-stage procedure: in the first stage
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Activity Detection for Massive Connectivity in Cell-Free Networks With Unknown Large-Scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-02-01 Hao Zhang, Qingfeng Lin, Yang Li, Lei Cheng, Yik-Chung Wu
Activity detection is an important task in the next generation grant-free multiple access. While there are a number of existing algorithms designed for this purpose, they mostly require precise information about the network, such as large-scale fading coefficients, small-scale fading channel statistics, noise variance at the access points, and user activity probability. Acquiring these information
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Nested Tensor-Based Integrated Sensing and Communication in RIS-Assisted THz MIMO Systems IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-29 Jianhe Du, Yuan Cheng, Libiao Jin, Shufeng Li, Feifei Gao
In this paper, we propose a nested tensor-based algorithm for integrated sensing and communication (ISAC) in reconfigurable intelligent surface (RIS)-assisted downlink terahertz (THz) multiple-input multiple-output (MIMO) systems. By exploiting the multi-group Khatri-Rao space-time (KRST) coding scheme at the base station (BS) and the structure of RIS phase shifts, we formulate the received signal
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Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-29 Mingjie Shao, Wing-Kin Ma, Junbin Liu, Zihao Huang
In this paper we study the expectation maximization (EM) technique for one-bit MIMO-OFDM detection (OMOD). Arising from the recent interest in massive MIMO with one-bit analog-to-digital converters, OMOD is a massive-scale problem. EM is an iterative method that can exploit the OFDM structure to process the problem in a per-iteration efficient fashion. In this study we analyze the convergence rate
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Federated Inference With Reliable Uncertainty Quantification Over Wireless Channels via Conformal Prediction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-29 Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Osvaldo Simeone
In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical information about their local data to the server over a common wireless channel, aiming to enhance the quality of the inference decision at the server. Recent work has introduced federated conformal prediction (CP), which leverages
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Signal Detection for Ultra-Massive MIMO: An Information Geometry Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-29 Jiyuan Yang, Yan Chen, Xiqi Gao, Dirk T. M. Slock, Xiang-Gen Xia
In this paper, we propose an information geometry approach (IGA) for signal detection (SD) in ultra-massive multiple-input multiple-output (MIMO) systems. We formulate the signal detection as obtaining the marginals of the a posteriori probability distribution of the transmitted symbol vector. Then, a maximization of the a posteriori marginals (MPM) for signal detection can be performed. With the information
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On the False Alarm Probability of the Normalized Matched Filter for Off-Grid Targets: A Geometrical Approach and Its Validity Conditions IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-25 Pierre Develter, Jonathan Bosse, Olivier Rabaste, Philippe Forster, Jean-Philippe Ovarlez
Off-grid targets are known to induce a mismatch that dramatically impacts the detection probability of the popular Normalized Matched Filter. To overcome this problem, the unknown target parameter is usually estimated through a Maximum Likelihood strategy, resulting in a GLRT (Generalized Likelihood Ratio Test) detection scheme. While the test statistic for the null hypothesis is well known in the
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UNO: Unlimited Sampling Meets One-Bit Quantization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-25 Arian Eamaz, Kumar Vijay Mishra, Farhang Yeganegi, Mojtaba Soltanalian
Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the
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Mixed-ADC Based PMCW MIMO Radar Angle-Doppler Imaging IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-25 Xiaolei Shang, Ronghao Lin, Yuanbo Cheng
Phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radar systems are known to possess excellent mutual interference mitigation capabilities, but require costly and power-hungry high sampling rate and high-precision analog-to-digital converters (ADC's). To reduce cost and power consumption, we consider a mixed-ADC architecture, in which most receive antenna outputs are sampled
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Information Flow Rate for Cross-Correlated Stochastic Processes IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-01-25 Dionissios T. Hristopulos
Causal inference seeks to identify cause-and-effect interactions in coupled systems. A recently proposed method by Liang detects causal relations by quantifying the direction and magnitude of information flow between time series. The theoretical formulation of information flow for stochastic dynamical systems provides a general expression and a data-driven statistic for the rate of entropy transfer