2021 IEEE Information Theory Workshop (ITW)最新文献_第8页

Polar Codes for Biometric Identification and Authentication 用于生物识别和认证的极性代码

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611417

Linghui Zhou, T. Oechtering, M. Skoglund

引用次数: 3

Refined Convergence Rates of the Good-Turing Estimator Good-Turing估计器的改进收敛速率

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611389

Amichai Painsky

引用次数: 6

The Achievable Rate Region of Wyner-Ahlswede-Körner Coding Problem for Mixed Sources 混合源Wyner-Ahlswede-Körner编码问题的可达率域

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611480

Daisuke Takeuchi, Shun Watanabe

引用次数: 2

Unsourced Random Access Based on List Recoverable Codes Correcting t Errors 基于列表可恢复码纠错的无源随机接入

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611447

K. Andreev, P. Rybin, A. Frolov

引用次数: 7

Attention-Based Hybrid Precoding for mmWave MIMO Systems 基于注意的毫米波MIMO系统混合预编码

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611432

Hao Jiang, Yu Lu, Xueru Li, Bichai Wang, Yongxing Zhou, L. Dai

{"title":"Attention-Based Hybrid Precoding for mmWave MIMO Systems","authors":"Hao Jiang, Yu Lu, Xueru Li, Bichai Wang, Yongxing Zhou, L. Dai","doi":"10.1109/ITW48936.2021.9611432","DOIUrl":"https://doi.org/10.1109/ITW48936.2021.9611432","url":null,"abstract":"Hybrid precoding design is a high-complexity problem due to the coupling of analog and digital precoders as well as the constant modulus constraint for the analog precoder. Fortunately, the deep learning based hybrid precoding methods can significantly reduce the complexity, but the performance remains limited. In this paper, inspired by the attention mechanism recently developed for machine learning, we propose an attention-based hybrid precoding scheme for millimeter-wave (mmWave) MIMO systems with improved performance and low complexity. The key idea is to design each user’s beam pattern according to its attention weights to other users’. Specifically, the proposed attention-based hybrid precoding scheme consists of two parts, i.e., the attention layer and the convolutional neural network (CNN) layer. The attention layer is used to identify the features of inter-user interferences. Then, these features are processed by the CNN layer for the analog precoder design to maximize the achievable sum-rate. Simulation results demonstrate that the attention layer could mitigate the inter-user interferences, and the proposed attention-based hybrid precoding with low complexity can achieve higher achievable sum-rate than the existing deep learning based method.","PeriodicalId":325229,"journal":{"name":"2021 IEEE Information Theory Workshop (ITW)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114680870","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

Single-Server Individually-Private Information Retrieval: A Combinatorial Approach 单服务器个人私有信息检索:一种组合方法

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611358

A. Heidarzadeh, A. Sprintson

{"title":"Single-Server Individually-Private Information Retrieval: A Combinatorial Approach","authors":"A. Heidarzadeh, A. Sprintson","doi":"10.1109/ITW48936.2021.9611358","DOIUrl":"https://doi.org/10.1109/ITW48936.2021.9611358","url":null,"abstract":"This paper considers the problem of single-server Individually-Private Information Retrieval (IPIR). In this problem, a user wants to retrieve D messages belonging to a dataset of K messages stored on a single server. Initially, the user knows M other messages belonging to the dataset as side information, where the identities of these M messages are unknown to the server. The goal is to minimize the total amount of information that the user must download from the server while keeping the identity of each of the D desired messages individually private, i.e., the identity of every individual message wanted by the user must be protected. The capacity of IPIR, which is defined as the supremum of all achievable download rates, was previously characterized for D = 2, M = 1. However, the capacity was left open for all other values of D, M. In this work, we present a technique for the proof of converse, based on a novel combinatorial approach. Using this technique, we establish an upper bound on the capacity of IPIR for D = 2, M = 2. For this setting, we also propose a new IPIR scheme—based on a probabilistic partitioning of the messages, that achieves the capacity upper bound. We believe that our approach can be employed for proving the converse and designing optimal schemes for the general cases of the problem.","PeriodicalId":325229,"journal":{"name":"2021 IEEE Information Theory Workshop (ITW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134370146","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

Hyperparameter Learning of Stochastic Image Generative Models with Bayesian Hierarchical Modeling and Its Effect on Lossless Image Coding 基于贝叶斯分层建模的随机图像生成模型的超参数学习及其对无损图像编码的影响

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611418

Yuta Nakahara, T. Matsushima

{"title":"Hyperparameter Learning of Stochastic Image Generative Models with Bayesian Hierarchical Modeling and Its Effect on Lossless Image Coding","authors":"Yuta Nakahara, T. Matsushima","doi":"10.1109/ITW48936.2021.9611418","DOIUrl":"https://doi.org/10.1109/ITW48936.2021.9611418","url":null,"abstract":"Explicit assumption of stochastic data generative models is a remarkable feature of lossless compression of general data in information theory. However, current lossless image coding mostly focus on coding procedures without explicit assumption of the stochastic generative model. Therefore, we have difficulty discussing the theoretical optimality of the coding procedure to the stochastic generative model. In this paper, we solve this difficulty by constructing a stochastic generative model by interpreting the previous coding procedure from another perspective. An important problem of our approach is how to learn the hyperparameters of the stochastic generative model because the optimality of our coding algorithm is guaranteed only asymptotically and the hyperparameter setting still affects the expected code length for finite length data. For this problem, we use Bayesian hierarchical modeling and confirm its effect by numerical experiments. In lossless image coding, this is the first study assuming such an explicit stochastic generative model and learning its hyperparameters, to the best of our knowledge.","PeriodicalId":325229,"journal":{"name":"2021 IEEE Information Theory Workshop (ITW)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134452386","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

Linear Programming Bounds on the Kissing Number of q-ary Codes q元码接吻数的线性规划界

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611478

P. Solé, Yi Liu, Wei Cheng, S. Guilley, O. Rioul

引用次数: 6

Hybrid Encryption in Correlated Randomness Model 关联随机模型中的混合加密

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611471

R. Safavi-Naini, Setareh Sharifian

引用次数: 0

Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach 泊松过程的速率畸变问题:一种群论方法

2021 IEEE Information Theory Workshop (ITW) Pub Date : 2021-10-17 DOI: 10.1109/ITW48936.2021.9611405

Hui-An Shen, S. M. Moser, J. Pfister

引用次数: 1