IEEE journal on selected areas in information theory最新文献_第10页

Efficient Algorithms for the Bee-Identification Problem 蜜蜂识别问题的有效算法

IEEE journal on selected areas in information theory Pub Date : 2023-07-18 DOI: 10.1109/JSAIT.2023.3296077

Han Mao Kiah;Alexander Vardy;Hanwen Yao

{"title":"Efficient Algorithms for the Bee-Identification Problem","authors":"Han Mao Kiah;Alexander Vardy;Hanwen Yao","doi":"10.1109/JSAIT.2023.3296077","DOIUrl":"10.1109/JSAIT.2023.3296077","url":null,"abstract":"The bee-identification problem, formally defined by Tandon, Tan, and Varshney (2019), requires the receiver to identify “bees” using a set of unordered noisy measurements. In this previous work, Tandon, Tan, and Varshney studied error exponents and showed that decoding the measurements jointly results in a significantly larger error exponent. In this work, we study algorithms related to this joint decoder. First, we demonstrate how to perform joint decoding efficiently. By reducing to the problem of finding perfect matching and minimum-cost matchings, we obtain joint decoders that run in time quadratic and cubic in the number of “bees” for the binary erasure (BEC) and binary symmetric channels (BSC), respectively. Next, by studying the matching algorithms in the context of channel coding, we further reduce the running times by using classical tools like peeling decoders and list-decoders. In particular, we show that our identifier algorithms when used with Reed-Muller codes terminate in almost linear and quadratic time for BEC and BSC, respectively. Finally, for explicit codebooks, we study when these joint decoders fail to identify the “bees” correctly. Specifically, we provide practical methods of estimating the probability of erroneous identification for given codebooks.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45416529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Active Privacy-Utility Trade-Off Against Inference in Time-Series Data Sharing 时间序列数据共享中的主动隐私效用权衡

IEEE journal on selected areas in information theory Pub Date : 2023-06-28 DOI: 10.1109/JSAIT.2023.3287929

Ecenaz Erdemir;Pier Luigi Dragotti;Deniz Gündüz

{"title":"Active Privacy-Utility Trade-Off Against Inference in Time-Series Data Sharing","authors":"Ecenaz Erdemir;Pier Luigi Dragotti;Deniz Gündüz","doi":"10.1109/JSAIT.2023.3287929","DOIUrl":"10.1109/JSAIT.2023.3287929","url":null,"abstract":"Internet of Things devices have become highly popular thanks to the services they offer. However, they also raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. We model the user’s personal information as the secret variable, to be kept private from an honest-but-curious service provider, and the useful variable, to be disclosed for utility. We consider an active learning framework, where one out of a finite set of measurement mechanisms is chosen at each time step, each revealing some information about the underlying secret and useful variables, albeit with different statistics. The measurements are taken such that the correct value of useful variable can be detected quickly, while the confidence on the secret variable remains below a predefined level. For privacy measure, we consider both the probability of correctly detecting the secret variable value and the mutual information between the secret and released data. We formulate both problems as partially observable Markov decision processes, and numerically solve by advantage actor-critic deep reinforcement learning. We evaluate the privacy-utility trade-off of the proposed policies on both the synthetic and real-world time-series datasets.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"159-173"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49236323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

SPRT-Based Efficient Best Arm Identification in Stochastic Bandits 基于sprt的随机盗匪有效最佳臂识别

IEEE journal on selected areas in information theory Pub Date : 2023-06-23 DOI: 10.1109/JSAIT.2023.3288988

Arpan Mukherjee;Ali Tajer

{"title":"SPRT-Based Efficient Best Arm Identification in Stochastic Bandits","authors":"Arpan Mukherjee;Ali Tajer","doi":"10.1109/JSAIT.2023.3288988","DOIUrl":"10.1109/JSAIT.2023.3288988","url":null,"abstract":"This paper investigates the best arm identification (BAI) problem in stochastic multi-armed bandits in the fixed confidence setting. The general class of the exponential family of bandits is considered. The existing algorithms for the exponential family of bandits face computational challenges. To mitigate these challenges, the BAI problem is viewed and analyzed as a sequential composite hypothesis testing task, and a framework is proposed that adopts the likelihood ratio-based tests known to be effective for sequential testing. Based on this test statistic, a BAI algorithm is designed that leverages the canonical sequential probability ratio tests for arm selection and is amenable to tractable analysis for the exponential family of bandits. This algorithm has two key features: (1) its sample complexity is asymptotically optimal, and (2) it is guaranteed to be \u0000<inline-formula> <tex-math>$delta -$ </tex-math></inline-formula>\u0000PAC. Existing efficient approaches focus on the Gaussian setting and require Thompson sampling for the arm deemed the best and the challenger arm. Additionally, this paper analytically quantifies the computational expense of identifying the challenger in an existing approach. Finally, numerical experiments are provided to support the analysis.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"128-143"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44500225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Dual-Blind Deconvolution for Overlaid Radar-Communications Systems 地面雷达通信系统的双盲反卷积

IEEE journal on selected areas in information theory Pub Date : 2023-06-22 DOI: 10.1109/JSAIT.2023.3287823

Edwin Vargas;Kumar Vijay Mishra;Roman Jacome;Brian M. Sadler;Henry Arguello

{"title":"Dual-Blind Deconvolution for Overlaid Radar-Communications Systems","authors":"Edwin Vargas;Kumar Vijay Mishra;Roman Jacome;Brian M. Sadler;Henry Arguello","doi":"10.1109/JSAIT.2023.3287823","DOIUrl":"10.1109/JSAIT.2023.3287823","url":null,"abstract":"The increasingly crowded spectrum has spurred the design of joint radar-communications systems that share hardware resources and efficiently use the radio frequency spectrum. We study a general spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this \u0000<italic>dual-blind deconvolution</i>\u0000 (DBD) problem, a common receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets. The communications and radar channels are represented by \u0000<italic>continuous-valued</i>\u0000 range-time and Doppler velocities of multiple transmission paths and multiple targets. We exploit the sparsity of both channels to solve the highly ill-posed DBD problem by casting it into a sum of multivariate atomic norms (SoMAN) minimization. We devise a semidefinite program to estimate the unknown target and communications parameters using the theories of positive-hyperoctant trigonometric polynomials (PhTP). Our theoretical analyses show that the minimum number of samples required for near-perfect recovery is dependent on the logarithm of the maximum of number of radar targets and communications paths rather than their sum. We show that our SoMAN method and PhTP formulations are also applicable to more general scenarios such as unsynchronized transmission, the presence of noise, and multiple emitters. Numerical experiments demonstrate great performance enhancements during parameter recovery under different scenarios.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"75-93"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45365256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

An Information-Theoretic Approach to Collaborative Integrated Sensing and Communication for Two-Transmitter Systems 双发射机系统协同集成传感与通信的信息论方法

IEEE journal on selected areas in information theory Pub Date : 2023-06-16 DOI: 10.1109/JSAIT.2023.3286932

Mehrasa Ahmadipour;Michèle Wigger

引用次数: 1

Continuous-Time Modeling and Analysis of Particle Beam Metrology 粒子束计量的连续时间建模与分析

IEEE journal on selected areas in information theory Pub Date : 2023-06-09 DOI: 10.1109/JSAIT.2023.3283911

Akshay Agarwal;Minxu Peng;Vivek K Goyal

{"title":"Continuous-Time Modeling and Analysis of Particle Beam Metrology","authors":"Akshay Agarwal;Minxu Peng;Vivek K Goyal","doi":"10.1109/JSAIT.2023.3283911","DOIUrl":"10.1109/JSAIT.2023.3283911","url":null,"abstract":"Particle beam microscopy (PBM) performs nanoscale imaging by pixelwise capture of scalar values representing noisy measurements of the response from secondary electrons (SEs) integrated over a dwell time. Extended to metrology, goals include estimating SE yield at each pixel and detecting differences in SE yield across pixels; obstacles include shot noise in the particle source as well as lack of knowledge of and variability in the instrument response to single SEs. A recently introduced time-resolved measurement paradigm promises mitigation of source shot noise, but its analysis and development have been largely limited to estimation problems under an idealization in which SE bursts are directly and perfectly counted. Here, analyses are extended to error exponents in feature detection problems and to degraded measurements that are representative of actual instrument behavior for estimation problems. For estimation from idealized SE counts, insights on existing estimators and a superior estimator are also provided. For estimation in a realistic PBM imaging scenario, extensions to the idealized model are introduced, methods for model parameter extraction are discussed, and large improvements from time-resolved data are presented.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"61-74"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43445733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sketching Low-Rank Matrices With a Shared Column Space by Convex Programming 用凸规划绘制共享列空间的低秩矩阵

IEEE journal on selected areas in information theory Pub Date : 2023-06-07 DOI: 10.1109/JSAIT.2023.3283973

Rakshith S. Srinivasa;Seonho Kim;Kiryung Lee

{"title":"Sketching Low-Rank Matrices With a Shared Column Space by Convex Programming","authors":"Rakshith S. Srinivasa;Seonho Kim;Kiryung Lee","doi":"10.1109/JSAIT.2023.3283973","DOIUrl":"10.1109/JSAIT.2023.3283973","url":null,"abstract":"In many practical applications including remote sensing, multi-task learning, and multi-spectrum imaging, data are described as a set of matrices sharing a common column space. We consider the joint estimation of such matrices from their noisy linear measurements. We study a convex estimator regularized by a pair of matrix norms. The measurement model corresponds to block-wise sensing and the reconstruction is possible only when the total energy is well distributed over blocks. The first norm, which is the maximum-block-Frobenius norm, favors such a solution. This condition is analogous to the notion of low-spikiness in matrix completion or column-wise sensing. The second norm, which is a tensor norm on a pair of suitable Banach spaces, induces low-rankness in the solution together with the first norm. We demonstrate that the joint estimation provides a significant gain over the individual recovery of each matrix when the number of matrices sharing a column space and the ambient dimension of the shared column space are large relative to the number of columns in each matrix. The convex estimator is cast as a semidefinite program and an efficient ADMM algorithm is derived. The empirical behavior of the convex estimator is illustrated using Monte Carlo simulations and recovery performance is compared to existing methods in the literature.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"54-60"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43436041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local Geometry of Nonconvex Spike Deconvolution From Low-Pass Measurements 低通测量非凸尖峰反卷积的局部几何

IEEE journal on selected areas in information theory Pub Date : 2023-03-30 DOI: 10.1109/JSAIT.2023.3262689

Maxime Ferreira Da Costa;Yuejie Chi

{"title":"Local Geometry of Nonconvex Spike Deconvolution From Low-Pass Measurements","authors":"Maxime Ferreira Da Costa;Yuejie Chi","doi":"10.1109/JSAIT.2023.3262689","DOIUrl":"10.1109/JSAIT.2023.3262689","url":null,"abstract":"Spike deconvolution is the problem of recovering the point sources from their convolution with a known point spread function, which plays a fundamental role in many sensing and imaging applications. In this paper, we investigate the local geometry of recovering the parameters of point sources—including both amplitudes and locations—by minimizing a natural nonconvex least-squares loss function measuring the observation residuals. We propose preconditioned variants of gradient descent (GD), where the search direction is scaled via some carefully designed preconditioning matrices. We begin with a simple fixed preconditioner design, which adjusts the learning rates of the locations at a different scale from those of the amplitudes, and show it achieves a linear rate of convergence—in terms of \u0000<italic>entrywise</i>\u0000 errors—when initialized close to the ground truth, as long as the separation between the true spikes is sufficiently large. However, the convergence rate slows down significantly when the dynamic range of the source amplitudes is large. To bridge this issue, we introduce an adaptive preconditioner design, which compensates for the learning rates of different sources in an iteration-varying manner based on the current estimate. The adaptive design provably leads to an accelerated convergence rate that is independent of the dynamic range, highlighting the benefit of adaptive preconditioning in nonconvex spike deconvolution. Numerical experiments are provided to corroborate the theoretical findings.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41864076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating the Sizes of Binary Error-Correcting Constrained Codes 二值纠错约束码的大小估计

IEEE journal on selected areas in information theory Pub Date : 2023-03-23 DOI: 10.1109/JSAIT.2023.3279113

V. Arvind Rameshwar;Navin Kashyap

{"title":"Estimating the Sizes of Binary Error-Correcting Constrained Codes","authors":"V. Arvind Rameshwar;Navin Kashyap","doi":"10.1109/JSAIT.2023.3279113","DOIUrl":"10.1109/JSAIT.2023.3279113","url":null,"abstract":"In this paper, we study binary constrained codes that are resilient to bit-flip errors and erasures. In our first approach, we compute the sizes of constrained subcodes of linear codes. Since there exist well-known linear codes that achieve vanishing probabilities of error over the binary symmetric channel (which causes bit-flip errors) and the binary erasure channel, constrained subcodes of such linear codes are also resilient to random bit-flip errors and erasures. We employ a simple identity from the Fourier analysis of Boolean functions, which transforms the problem of counting constrained codewords of linear codes to a question about the structure of the dual code. We illustrate the utility of our method in providing explicit values or efficient algorithms for our counting problem, by showing that the Fourier transform of the indicator function of the constraint is computable, for different constraints. Our second approach is to obtain good upper bounds, using an extension of Delsarte’s linear program (LP), on the largest sizes of constrained codes that can correct a fixed number of combinatorial errors or erasures. We observe that the numerical values of our LP-based upper bounds beat the generalized sphere packing bounds of Fazeli et al. (2015).","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"144-158"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135180978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Spatio-Temporal Wildfire Prediction Using Multi-Modal Data 基于多模态数据的野火时空预测

IEEE journal on selected areas in information theory Pub Date : 2023-03-22 DOI: 10.1109/JSAIT.2023.3276054

Chen Xu;Yao Xie;Daniel A. Zuniga Vazquez;Rui Yao;Feng Qiu

{"title":"Spatio-Temporal Wildfire Prediction Using Multi-Modal Data","authors":"Chen Xu;Yao Xie;Daniel A. Zuniga Vazquez;Rui Yao;Feng Qiu","doi":"10.1109/JSAIT.2023.3276054","DOIUrl":"10.1109/JSAIT.2023.3276054","url":null,"abstract":"Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (such as state governments and power utility companies) to achieve a more informed understanding of wildfire activities and plan preventive measures. A desirable algorithm should precisely predict fire risk and magnitude for a location in real time. In this paper, we develop a flexible spatio-temporal wildfire prediction framework using multi-modal time series data. We first predict the wildfire risk (the chance of a wildfire event) in real-time, considering the historical events using discrete mutually exciting point process models. Then we further develop a wildfire magnitude prediction set method based on the flexible distribution-free time-series conformal prediction (CP) approach. Theoretically, we prove a risk model parameter recovery guarantee, as well as coverage and set size guarantees for the CP sets. Through extensive real-data experiments with wildfire data in California, we demonstrate the effectiveness of our methods, as well as their flexibility and scalability in large regions.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"4 ","pages":"302-313"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42615184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0