Collaborative Research: CCSS: Coding for 5G and Beyond: Limits and Efficient Algorithms

在接下来的几年里，人均数据使用量将呈指数级增长。例如，手机行业预测，全球人均信息使用量将从2014年的15 GB增长到2019年的37 GB左右。如果没有显著的技术进步来增加其容量，现有的电信基础设施将无法支持这一巨大的数据增长。现代纠错码（诸如turbo码、低密度奇偶校验码和极化码）的出现代表了无线系统的纠错性能的巨大飞跃，允许以接近容量的数据速率在噪声信道上可靠地通信信息。然而，在这个方向上的改进主要限于点对点情况，并且使点对点信道设置的增益最大化将不足以满足新兴通信系统（特别是5G蜂窝系统和更高）的高吞吐量和低延迟要求。该项目旨在通过将编码方案扩展到无线网络设置来解决这些问题，从而产生超出经典点对点情况的额外吞吐量增益。该研究有望为许多关键应用提供重大的变革性影响，这些应用采用可靠的网络无线通信，例如在医疗保健，环境监测，金融等领域。在该项目中，提出了一个实用的低复杂度，低延迟，容量接近稀疏图码的综合框架。这些代码能够利用网络增益进行纠错，从而显著减少传输的数据量。该项目的目的是研究这些计划的基本限制，以及调查实际的编码算法，以接近这些限制。拟议的研究涉及与稀疏图基和极化码应用于新兴的未来通信系统相关的几个基本主题，这些主题在以前的研究中不存在：分析和设计用于规范网络通信问题的嵌套码;分析性能如何随各种代码和解码器设计参数而变化;代数码和随机码之间的实现复杂性与性能权衡的理论理解;迭代解码失败事件的分析调查;以及一种新型高速现场可编程门阵列硬件解码结构的开发。

During the next couple of years, an exponential increase of data use per capita will be experienced. For example, predictions by the cellular industry state that the global per-capita information usage will grow from 15 GB in 2014 to around 37 GB in 2019. Without significant technological advances to increase its capacity, the existing telecommunications infrastructure will be unable to support this vast data increase. The advent of modern error-correcting codes, such as turbo codes, low-density parity-check codes, and polar codes, has represented a quantum leap in error-correcting performance for wireless systems, allowing reliable communication of information over noisy channels at data rates close to capacity. However, improvements in this direction have been mostly limited to the point-to-point case, and maximizing gains for the point-to-point channel setup will not be sufficient to satisfy the high throughput and low delay requirements of emerging communication systems, in particular of 5G cellular systems and beyond. This project aims to tackle these issues by extending coding schemes to the wireless network setting, thereby yielding additional throughput gains beyond the classical point-to-point case. The proposed research promises to provide a significant transformative impact on many critical applications employing reliable networked wireless communication, for example in the fields of healthcare, environmental monitoring, finance, and so on.In this project, a comprehensive framework of practical low-complexity, low-latency, capacity-approaching sparse graph codes is proposed. These codes are able to leverage network gains for error correction, thereby significantly reducing the amount of transmitted data. The project aims to study the fundamental limits of these schemes as well as to investigate practical coding algorithms to approach these limits. The proposed research involves several fundamental themes related to the application of sparse graph-based and polar codes to emerging future communication systems which are not present in previous studies: analysis and design of nested codes for canonical network communication problems; analysis of how performance scales with various code and decoder design parameters; a theoretical understanding of the implementation complexity versus performance trade-offs between algebraic and random codes; an analytical investigation of iterative decoding failure events; and the development of a novel high-speed field-programmable gate array hardware decoding architecture.