2019 IEEE International Symposium on Information Theory (ISIT)最新文献_第6页

Error Exponents of Parallel Two-way Discrete Memoryless Channels using Variable Length Coding 采用变长编码的并行双向离散无记忆信道的误差指数

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849727

K. Palacio-Baus, Meysam Asadi, N. Devroye

引用次数: 2

Low-degree Pseudo-Boolean Function Recovery Using Codes 使用代码的低次伪布尔函数恢复

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849424

Orhan Ocal, S. Kadhe, K. Ramchandran

引用次数: 4

SPARCs and AMP for Unsourced Random Access 无源随机访问的SPARCs和AMP

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849802

Alexander Fengler, P. Jung, G. Caire

引用次数: 56

Interactions Between Learning and Broadcasting in Wireless Recommendation Systems 无线推荐系统中学习与广播的交互作用

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849556

Linqi Song, C. Fragouli, D. Shah

引用次数: 7

Towards an Algebraic Network Information Theory: Distributed Lossy Computation of Linear Functions 迈向代数网络信息论:线性函数的分布有损计算

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849363

S. Lim, Chen Feng, A. Pastore, B. Nazer, M. Gastpar

引用次数: 5

Pareto Optimal Schemes in Coded Caching 编码缓存中的Pareto最优方案

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849680

P. VijithKumarK., B. K. Rai, T. Jacob

引用次数: 4

Constrained de Bruijn Codes and their Applications 约束de Bruijn码及其应用

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849237

Y. M. Chee, T. Etzion, H. M. Kiah, Van Khu Vu, Eitan Yaakobi

引用次数: 4

Convolutional Decoding of Polar Codes 极性码的卷积解码

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849752

Arman Fazeli, A. Vardy, Hanwen Yao

{"title":"Convolutional Decoding of Polar Codes","authors":"Arman Fazeli, A. Vardy, Hanwen Yao","doi":"10.1109/ISIT.2019.8849752","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849752","url":null,"abstract":"Polar coding has found its way into many realms in communications and information theory. In most implementation setups, they are accompanied with the list successive cancellation (LSC) decoding algorithm which is shown to provide a superior error performance compared to the original successive cancellation (SC) decoding method. While the SC decoding is fairly well-studied, the exact math behind LSC’s superior performance still remains to be of mystery. Multiple techniques have been proposed to further improve the LSC’s error performance or to reduce its computational complexity, which are usually motivated by heuristic reasons and shown through numerical simulations. Most notable example is the CRC-aided LSC, which drastically improves the LSC’s performance by concatenating the polar code with some high-rate cyclic redundancy check (CRC) codes.In this paper, we present polar codes that are concatenated with an underlying high-rate convolutional code, which are shown to have superior performances over CRC-aided LSC. We also present a computationally-efficient decoding algorithm for these codes which resembles the techniques used in the Viterbi algorithm, and hence is called the convolutional decoding algorithm. To do this, we revisit the error analysis of the original SC decoding along with the concept of Arıkan’s helper genie. We address some shortcomings of the CRC-aided LSC and discuss how to turn around them by emulating a convolutional code instead of a CRC code. Contrary to CRC codes, most of the convolutional codes are not a proper choice for concatenation with polar codes. We introduce the bucketing algorithm to construct suitable punctured convolutional codes for this purpose. The proposed framework can accommodate any such underlying convolutional code, which allows one to search for the optimal convolutional code based on their design parameters.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"75 1","pages":"1397-1401"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78809768","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

Sparse Covariance Estimation from Quadratic Measurements: A Precise Analysis 二次测量的稀疏协方差估计:一个精确的分析

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849405

Ehsan Abbasi, Fariborz Salehi, B. Hassibi

{"title":"Sparse Covariance Estimation from Quadratic Measurements: A Precise Analysis","authors":"Ehsan Abbasi, Fariborz Salehi, B. Hassibi","doi":"10.1109/ISIT.2019.8849405","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849405","url":null,"abstract":"We study the problem of estimating a high-dimensional sparse covariance matrix, Σ0, from a finite number of quadratic measurements, i.e., measurements ${text{a}}_i^T{Sigma _0}{{text{a}}_i}$ which are quadratic forms in the measurement vectors ai resulting from the covariance matrix, Σ0. Such a problem arises in applications where we can only make energy measurements of the underlying random variables. We study a simple LASSO-like convex recovery algorithm which involves a squared 2-norm (to match the covariance estimate to the measurements), plus a regularization term (that penalizes the ℓ1−norm of the non-diagonal entries of Σ0 to enforce sparsity). When the measurement vectors are i.i.d. Gaussian, we obtain the precise error performance of the algorithm (accurately determining the estimation error in any metric, e.g., 2-norm, operator norm, etc.) as a function of the number of measurements and the underlying distribution of Σ0. In particular, in the noiseless case we determine the necessary and sufficient number of measurements required to perfectly recover Σ0 as a function of its sparsity. Our results rely on a novel comparison lemma which relates a convex optimization problem with \"quadratic Gaussian\" measurements to one which has i.i.d. Gaussian measurements.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"15 1","pages":"2074-2078"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76260610","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: 1

Second-Order Characterizations via Partial Smoothing 通过部分平滑的二阶表征

2019 IEEE International Symposium on Information Theory (ISIT) Pub Date : 2019-07-07 DOI: 10.1109/ISIT.2019.8849622

Anurag Anshu, M. Berta, Rahul Jain, M. Tomamichel

引用次数: 0