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CIF: Small: Learning To Correct Errors

CIF：小：学习纠正错误

基本信息

批准号：
2100013
负责人：
Bane Vasic
金额：
$ 49.14万
依托单位：
University of Arizona
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100013&HistoricalAwards=false
关键词：
CIF Small Learning Correct Errors

项目摘要

Error-correcting codes play an essential role in ensuring integrity of data in numerous applications, including wireless communications, computer networks and data storage. The beauty of error-correcting codes and elegance of their decoding algorithms reflect the rich structure of the underlying mathematical objects, but also human creativity and ingenuity. Motivated by recent successes of Deep Neural Networks (DNN) in various applications, this project is investigating if, how, and how efficiently this structure and elegance can be learned by a machine, and how the discovery of new and improved decoding algorithms can be automated. The project is devising parallelizable neural network based decoders with ultra-low latency and improved error performance. The resulting decoders are intended to find broad applicability in emerging wireless standards (5G and beyond) as well as new applications including decoding for quantum computers, machine-to-machine communications, long-haul optical communications and flash memories in data centers.This project approaches the error-correction problem as classification of the points in a high-dimensional signal space, and uses DNNs to learn complex codeword decision region boundaries to approach the maximum-likelihood decoding performance. The project addresses two key practical challenges: a) achieving extremely low misclassification rates (many orders of magnitude lower) compared to traditional machine learning applications, and b) maintaining low-complexity of decoding via sparse and quantized neural networks. The first thrust utilizes coding-theory domain knowledge, and harnesses the structural similarities between iterative decoders and neural networks for improved learning of decoders. This is achieved by using sampled error patterns from iterative decoding algorithms for stochastic optimization of neural-network decoders. The second thrust focuses on using a new supervised approach for learning decoders, in which existing high-complexity decoders are employed to synthesize a low-complexity neural-network-based decoder with comparable performance. The third thrust establishes theoretical results on the generalization capabilities of neural-network-based decoders (including convergence rates and error performance). This research is providing multi-faceted insight into modern error-correction algorithms and machine learning, and should lead to both theoretical and practical advances of importance to the communications and computer industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在包括无线通信、计算机网络和数据存储在内的众多应用中，纠错码在确保数据完整性方面起着至关重要的作用。纠错码的美丽和译码算法的优雅反映了底层数学对象的丰富结构，也反映了人类的创造力和聪明才智。受深度神经网络(DNN)最近在各种应用中取得的成功的启发，该项目正在研究机器是否、如何以及如何有效地学习这种结构和优雅，以及如何自动发现新的和改进的解码算法。该项目正在设计基于神经网络的可并行解码器，具有超低延迟和改进的差错性能。该项目将纠错问题归结为对高维信号空间中点的分类，并使用DNN学习复杂的码字判决区域边界，以逼近最大似然译码性能。该项目解决了两个关键的实际挑战：a)与传统的机器学习应用程序相比，实现极低的错误分类率(低许多个数量级)，以及b)通过稀疏和量化的神经网络保持较低的解码复杂度。第一个推力利用编码理论领域知识，并利用迭代译码和神经网络之间的结构相似性来改进译码的学习。这是通过使用用于神经网络解码器的随机优化的迭代解码算法的采样误差模式来实现的。第二个重点是使用一种新的有监督的方法来学习解码器，在这种方法中，利用现有的高复杂度解码器来综合具有类似性能的低复杂度的基于神经网络的解码器。第三个推力建立了关于基于神经网络的解码器的泛化能力(包括收敛速度和错误性能)的理论结果。这项研究为现代纠错算法和机器学习提供了多方面的见解，并应导致对通信和计算机行业具有重要意义的理论和实践进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（33）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning to Decode Linear Block Codes using Adaptive Gradient-Descent Bit-Flipping

学习使用自适应梯度下降位翻转解码线性块码

DOI：
10.1109/istc57237.2023.10273470
发表时间：
2023
期刊：
2023 12th International Symposium on Topics in Coding (ISTC
影响因子：
0
作者：
Milojković, Jovan;Brkic, Srdan;Ivaniš, Predrag;Vasić, Bane
通讯作者：
Vasić, Bane

Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

DOI：
10.1109/jsac.2021.3118408
发表时间：
2021-12
期刊：
IEEE Journal on Selected Areas in Communications
影响因子：
16.4
作者：
Mohamed Seif Eldin Mohamed-Mohamed-Seif-Eldin-Mohamed-2151239787;Wei-Ting Chang;Ravi Tandon
通讯作者：
Mohamed Seif Eldin Mohamed-Mohamed-Seif-Eldin-Mohamed-2151239787;Wei-Ting Chang;Ravi Tandon

Differentially Private Federated Learning with Drift Control

具有漂移控制的差分私有联邦学习