CAREER: Sparse Graph-Based Codes for Network Data Compression

在物联网(智能家居、可穿戴设备、联网汽车等)等新兴系统的推动下，社会正在以越来越快的速度生成和使用海量数据。例如，预计未来三年创造的数据量将超过过去30年创造的数据量。如果没有重大的技术进步，现有的通信基础设施将无法应对这种指数级的增长。该项目通过探索如何通过网络压缩此类数据来显著减少需要传输的流量来应对这一挑战。其主要思想是探索新的数据压缩方案，通过利用数据中的相似性和结构以及设备在网络中的连接方式来利用尚未开发的收益。例如，将电网不同位置的许多相关测量传输到一个目的地，或将视频回放传输到体育场中大量人群的单个设备。因此，拟议的研究有望对采用可靠的网络数据压缩的许多关键应用程序产生重大的变革影响，例如在医疗保健、环境监测和金融领域。该项目还包括一项综合教育计划，以增加科学、技术、工程和数学(STEM)的参与度，特别是在少数群体中。这一目标得到了几个相辅相成的倡议的支持，包括有针对性的K-12活动以及相关的教师培训和指导。拟议的研究通过采用网络编码、图论、迭代信息处理、机器学习和电路设计的思想，显著提高了网络数据压缩的最新水平。该项目涉及几个与网络感知、低复杂度和吞吐量高效的数据压缩方案有关的基本主题，这些方案在以前的研究中没有出现：有损信源编码的一般方案的理论分析和设计，涉及消息传递编码下的有限长度缩放特性的表征和有害图形子结构的分析；规范网络结构中嵌套图编码的基本率失真性能的调查，探索在实际空间耦合结构中在压缩方面可实现的网络收益；以及算法的进步和新颖的高速现场可编程门阵列硬件结构。该项目的理论成果有可能促进我们对编码策略的基本理解，从而在源代码压缩方面实现网络收益，打开新的机会和挑战。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。

Driven by emerging systems, such as the Internet-of-things (smart homes, wearables, connected cars, and so on), society is generating and using massive amounts of data at an ever increasing rate. For example, the amount of data created over the next three years is predicted to be more than that created over the past 30 years. Without significant technological advances, existing communications infrastructure will not be able to cope with this exponential increase. This project addresses this challenge by exploring how such data can be compressed over networks to significantly reduce the traffic that needs to be transmitted. The main idea is to explore new data-compression schemes that leverage untapped gains by exploiting similarities and structure in the data as well as how the devices are connected in the network. Examples include transmitting many related measurements in different locations of the power grid to a single destination, or transmitting a video replay to the individual devices of a large crowd in a sports stadium. As such, the proposed research promises to provide a significant transformative impact on many critical applications employing reliable networked data compression, for example in the fields of healthcare, environmental monitoring, and finance. The project also includes an integrated education plan to increase participation in Science, Technology, Engineering, and Mathematics (STEM), particularly among minority groups. This objective is supported by several complementary initiatives, including targeted K-12 activities as well as related teacher training and mentoring.The proposed research significantly advances the state of the art in network data compression by employing ideas from network coding, graph theory, iterative information processing, machine learning, and circuit design. The project involves several fundamental themes related to network-aware, low-complexity, and throughput-efficient data compression schemes which are not present in previous studies: a theoretical analysis and design of general schemes for lossy source coding, involving a characterization of finite-length scaling properties under message passing encoding and analysis of harmful graphical substructures; an investigation of the fundamental rate-distortion performance of nested graph-based codes in canonical network structures, exploring the achievable network gains in compression for practical spatially coupled constructions; and algorithmic advances and novel high-speed field programmable gate array hardware architectures. The theoretical results of this project have the potential to advance our fundamental understanding of coding strategies to achieve network gains in source compression, opening new opportunities and challenges.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.