CIF: Small: Fast Rate-Efficient Codes for Data Compression and Transmission via Sparse Regression

CIF：小型：通过稀疏回归进行数据压缩和传输的快速高效代码

• 批准号: 1217023
• 负责人: Sekhar Tatikonda
• 金额: $ 49.95万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217023&HistoricalAwards=false
• 关键词: CIF; Small; Fast; Rate; Efficient

Modern communication networks are constantly growing in size and sophistication. New applications require these networks to be reliable, computationally efficient, and have small latency. To meet these demands it is critical to have low-complexity, rate-efficient codes for communication and compression. Since Shannon's fundamental results, there has been a flurry of activity to design capacity achieving, computationally efficient coding schemes. For the channel coding problem, it was not until the early 1990s that capacity achieving codes were implemented. Similarly, many good quantizer designs were developed for lossy compression, but unfortunately none of these low-complexity codes provably attain the rate-distortion bound. The divergence between information-theoretic results and code construction is even more pronounced in network communication problems. Despite a sharp characterization of information-theoretic limits for several network models, the best practical codes for these problems fall short of the capacity limits.This research involves the development of computationally efficient codes for Gaussian sources and channels. To this end we leverage recent advances in high-dimensional sparse regression. These are the first low-complexity codes that provably attain the information-theoretic limits codes for Gaussian sources and channels. The main objectives of this research project are: (1) Determining the fundamental limits of sparse regression codes in a variety of communication theoretic settings; (2) Developing low-complexity encoding and decoding schemes for our sparse regression codes. This part of the project draws on ideas from function approximation and sparse signal recovery. The project also provides an opportunity for training graduate students and postdoctoral researchers in the disciplines of communication theory, data compression, statistics and networks.

现代通信网络的规模和复杂性不断增长。新的应用要求这些网络可靠，计算效率高，延迟小。为了满足这些需求，关键是要有低复杂度，高效率的通信和压缩代码。自从香农的基本结果，已经有一系列的活动来设计容量实现，计算效率的编码方案。对于信道编码问题，直到20世纪90年代初才实现容量实现码。类似地，许多好的量化器设计被开发用于有损压缩，但不幸的是，这些低复杂度代码中没有一个可证明达到率失真界限。信息论的结果和代码结构之间的分歧在网络通信问题中更加明显。尽管尖锐的表征的信息理论的限制，为这些问题的最佳实用代码不符合容量limits.This研究涉及高斯源和信道的计算效率的代码的发展。为此，我们利用高维稀疏回归的最新进展。这些是第一个低复杂度的代码，可证明达到高斯源和信道的信息理论极限码。本研究计画的主要目标为：（1）在各种通讯理论的设定下，决定稀疏回归码的基本极限;（2）发展我们稀疏回归码的低复杂度编码与译码方案。该项目的这一部分借鉴了函数逼近和稀疏信号恢复的想法。该项目还为培训通信理论、数据压缩、统计和网络等学科的研究生和博士后研究人员提供了机会。